million_songs_recommender.ipynb

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  "metadata": {
    "colab": {
      "name": "million_songs_recommender.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vwOS-lLUlf4C"
      },
      "source": [
        "### Import the necessary packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kv8JWGoFktFU"
      },
      "source": [
        "import os\n",
        "import warnings\n",
        "warnings.simplefilter(action = 'ignore', category=FutureWarning)\n",
        "warnings.filterwarnings('ignore')\n",
        "def ignore_warn(*args, **kwargs):\n",
        "    pass\n",
        "\n",
        "warnings.warn = ignore_warn #ignore annoying warning (from sklearn and seaborn)\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import time\n",
        "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from scipy.sparse import coo_matrix\n",
        "import math as mt\n",
        "from scipy.sparse.linalg import * #used for matrix multiplication\n",
        "from scipy.sparse.linalg import svds\n",
        "from scipy.sparse import csc_matrix\n",
        "from scipy.stats import skew, norm, probplot\n",
        "import seaborn as sns\n",
        "sns.set(style=\"ticks\", color_codes=True, font_scale=1.5)\n",
        "color = sns.color_palette()\n",
        "sns.set_style('darkgrid')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GnNo3-qxlp1c"
      },
      "source": [
        "### Popularity Recommender"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEX-LsO6lqUI"
      },
      "source": [
        "#Class for Popularity based Recommender System model\n",
        "class popularity_recommender_py():\n",
        "    def __init__(self):\n",
        "        self.train_data = None\n",
        "        self.user_id = None\n",
        "        self.item_id = None\n",
        "        self.popularity_recommendations = None\n",
        "        \n",
        "    #Create the popularity based recommender system model\n",
        "    def create(self, train_data, user_id, item_id):\n",
        "        self.train_data = train_data\n",
        "        self.user_id = user_id\n",
        "        self.item_id = item_id\n",
        "\n",
        "        #Get a count of user_ids for each unique song as recommendation score\n",
        "        train_data_grouped = train_data.groupby([self.item_id]).agg({self.user_id: 'count'}).reset_index()\n",
        "        train_data_grouped.rename(columns = {'user_id': 'score'},inplace=True)\n",
        "    \n",
        "        #Sort the songs based upon recommendation score\n",
        "        train_data_sort = train_data_grouped.sort_values(['score', self.item_id], ascending = [0,1])\n",
        "    \n",
        "        #Generate a recommendation rank based upon score\n",
        "        train_data_sort['Rank'] = train_data_sort['score'].rank(ascending=0, method='first')\n",
        "        \n",
        "        #Get the top 10 recommendations\n",
        "        self.popularity_recommendations = train_data_sort.head(10)\n",
        "\n",
        "    #Use the popularity based recommender system model to\n",
        "    #make recommendations\n",
        "    def recommend(self, user_id):    \n",
        "        user_recommendations = self.popularity_recommendations\n",
        "        \n",
        "        #Add user_id column for which the recommendations are being generated\n",
        "        user_recommendations['user_id'] = user_id\n",
        "    \n",
        "        #Bring user_id column to the front\n",
        "        cols = user_recommendations.columns.tolist()\n",
        "        cols = cols[-1:] + cols[:-1]\n",
        "        user_recommendations = user_recommendations[cols]\n",
        "        \n",
        "        return user_recommendations\n",
        "    \n",
        "\n",
        "#Class for Item similarity based Recommender System model\n",
        "class item_similarity_recommender_py():\n",
        "    def __init__(self):\n",
        "        self.train_data = None\n",
        "        self.user_id = None\n",
        "        self.item_id = None\n",
        "        self.cooccurence_matrix = None\n",
        "        self.songs_dict = None\n",
        "        self.rev_songs_dict = None\n",
        "        self.item_similarity_recommendations = None\n",
        "        \n",
        "    #Get unique items (songs) corresponding to a given user\n",
        "    def get_user_items(self, user):\n",
        "        user_data = self.train_data[self.train_data[self.user_id] == user]\n",
        "        user_items = list(user_data[self.item_id].unique())\n",
        "        \n",
        "        return user_items\n",
        "        \n",
        "    #Get unique users for a given item (song)\n",
        "    def get_item_users(self, item):\n",
        "        item_data = self.train_data[self.train_data[self.item_id] == item]\n",
        "        item_users = set(item_data[self.user_id].unique())\n",
        "            \n",
        "        return item_users\n",
        "        \n",
        "    #Get unique items (songs) in the training data\n",
        "    def get_all_items_train_data(self):\n",
        "        all_items = list(self.train_data[self.item_id].unique())\n",
        "            \n",
        "        return all_items\n",
        "        \n",
        "    #Construct cooccurence matrix\n",
        "    def construct_cooccurence_matrix(self, user_songs, all_songs):\n",
        "            \n",
        "        ####################################\n",
        "        #Get users for all songs in user_songs.\n",
        "        ####################################\n",
        "        user_songs_users = []        \n",
        "        for i in range(0, len(user_songs)):\n",
        "            user_songs_users.append(self.get_item_users(user_songs[i]))\n",
        "            \n",
        "        ###############################################\n",
        "        #Initialize the item cooccurence matrix of size \n",
        "        #len(user_songs) X len(songs)\n",
        "        ###############################################\n",
        "        cooccurence_matrix = np.matrix(np.zeros(shape=(len(user_songs), len(all_songs))), float)\n",
        "           \n",
        "        #############################################################\n",
        "        #Calculate similarity between user songs and all unique songs\n",
        "        #in the training data\n",
        "        #############################################################\n",
        "        for i in range(0,len(all_songs)):\n",
        "            #Calculate unique listeners (users) of song (item) i\n",
        "            songs_i_data = self.train_data[self.train_data[self.item_id] == all_songs[i]]\n",
        "            users_i = set(songs_i_data[self.user_id].unique())\n",
        "            \n",
        "            for j in range(0,len(user_songs)):       \n",
        "                    \n",
        "                #Get unique listeners (users) of song (item) j\n",
        "                users_j = user_songs_users[j]\n",
        "                    \n",
        "                #Calculate intersection of listeners of songs i and j\n",
        "                users_intersection = users_i.intersection(users_j)\n",
        "                \n",
        "                #Calculate cooccurence_matrix[i,j] as Jaccard Index\n",
        "                if len(users_intersection) != 0:\n",
        "                    #Calculate union of listeners of songs i and j\n",
        "                    users_union = users_i.union(users_j)\n",
        "                    \n",
        "                    cooccurence_matrix[j,i] = float(len(users_intersection))/float(len(users_union))\n",
        "                else:\n",
        "                    cooccurence_matrix[j,i] = 0\n",
        "                    \n",
        "        \n",
        "        return cooccurence_matrix\n",
        "\n",
        "    \n",
        "    #Use the cooccurence matrix to make top recommendations\n",
        "    def generate_top_recommendations(self, user, cooccurence_matrix, all_songs, user_songs):\n",
        "        print(\"Non zero values in cooccurence_matrix :%d\" % np.count_nonzero(cooccurence_matrix))\n",
        "        \n",
        "        #Calculate a weighted average of the scores in cooccurence matrix for all user songs.\n",
        "        user_sim_scores = cooccurence_matrix.sum(axis=0)/float(cooccurence_matrix.shape[0])\n",
        "        user_sim_scores = np.array(user_sim_scores)[0].tolist()\n",
        " \n",
        "        #Sort the indices of user_sim_scores based upon their value\n",
        "        #Also maintain the corresponding score\n",
        "        sort_index = sorted(((e,i) for i,e in enumerate(list(user_sim_scores))), reverse=True)\n",
        "    \n",
        "        #Create a dataframe from the following\n",
        "        columns = ['user_id', 'song', 'score', 'rank']\n",
        "        #index = np.arange(1) # array of numbers for the number of samples\n",
        "        df = pd.DataFrame(columns=columns)\n",
        "         \n",
        "        #Fill the dataframe with top 10 item based recommendations\n",
        "        rank = 1 \n",
        "        for i in range(0,len(sort_index)):\n",
        "            if ~np.isnan(sort_index[i][0]) and all_songs[sort_index[i][1]] not in user_songs and rank <= 10:\n",
        "                df.loc[len(df)]=[user,all_songs[sort_index[i][1]],sort_index[i][0],rank]\n",
        "                rank = rank+1\n",
        "        \n",
        "        #Handle the case where there are no recommendations\n",
        "        if df.shape[0] == 0:\n",
        "            print(\"The current user has no songs for training the item similarity based recommendation model.\")\n",
        "            return -1\n",
        "        else:\n",
        "            return df\n",
        " \n",
        "    #Create the item similarity based recommender system model\n",
        "    def create(self, train_data, user_id, item_id):\n",
        "        self.train_data = train_data\n",
        "        self.user_id = user_id\n",
        "        self.item_id = item_id\n",
        "\n",
        "    #Use the item similarity based recommender system model to\n",
        "    #make recommendations\n",
        "    def recommend(self, user):\n",
        "        \n",
        "        ########################################\n",
        "        #A. Get all unique songs for this user\n",
        "        ########################################\n",
        "        user_songs = self.get_user_items(user)    \n",
        "            \n",
        "        print(\"No. of unique songs for the user: %d\" % len(user_songs))\n",
        "        \n",
        "        ######################################################\n",
        "        #B. Get all unique items (songs) in the training data\n",
        "        ######################################################\n",
        "        all_songs = self.get_all_items_train_data()\n",
        "        \n",
        "        print(\"no. of unique songs in the training set: %d\" % len(all_songs))\n",
        "         \n",
        "        ###############################################\n",
        "        #C. Construct item cooccurence matrix of size \n",
        "        #len(user_songs) X len(songs)\n",
        "        ###############################################\n",
        "        cooccurence_matrix = self.construct_cooccurence_matrix(user_songs, all_songs)\n",
        "        \n",
        "        #######################################################\n",
        "        #D. Use the cooccurence matrix to make recommendations\n",
        "        #######################################################\n",
        "        df_recommendations = self.generate_top_recommendations(user, cooccurence_matrix, all_songs, user_songs)\n",
        "                \n",
        "        return df_recommendations\n",
        "    \n",
        "    #Get similar items to given items\n",
        "    def get_similar_items(self, item_list):\n",
        "        \n",
        "        user_songs = item_list\n",
        "        \n",
        "        ######################################################\n",
        "        #B. Get all unique items (songs) in the training data\n",
        "        ######################################################\n",
        "        all_songs = self.get_all_items_train_data()\n",
        "        \n",
        "        print(\"no. of unique songs in the training set: %d\" % len(all_songs))\n",
        "         \n",
        "        ###############################################\n",
        "        #C. Construct item cooccurence matrix of size \n",
        "        #len(user_songs) X len(songs)\n",
        "        ###############################################\n",
        "        cooccurence_matrix = self.construct_cooccurence_matrix(user_songs, all_songs)\n",
        "        \n",
        "        #######################################################\n",
        "        #D. Use the cooccurence matrix to make recommendations\n",
        "        #######################################################\n",
        "        user = \"\"\n",
        "        df_recommendations = self.generate_top_recommendations(user, cooccurence_matrix, all_songs, user_songs)\n",
        "         \n",
        "        return df_recommendations\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ummzW0pzPRQR"
      },
      "source": [
        "### Load dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yKtzyXFOPTNP"
      },
      "source": [
        "Prepare Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLTMNXbulqhj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "a9e6e3cd-cb40-4ccc-fe76-f3feb5ec6bc4"
      },
      "source": [
        "track_metadata_df = pd.read_csv('song_data.csv')\n",
        "count_play_df = pd.read_csv('10000.txt', sep='\\t', header=None, names=['user','song','play_count'])\n",
        "\n",
        "print('First see of track metadata:')\n",
        "print('Number of rows:', track_metadata_df.shape[0])\n",
        "print('Number of unique songs:', len(track_metadata_df.song_id.unique()))\n",
        "display(track_metadata_df.head())\n",
        "print('Note the problem with repeated track metadata. Let\\'s see of counts play song by users:')\n",
        "display(count_play_df.shape, count_play_df.head())\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First see of track metadata:\n",
            "Number of rows: 1000000\n",
            "Number of unique songs: 999056\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>song_id</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SOQMMHC12AB0180CB8</td>\n",
              "      <td>Silent Night</td>\n",
              "      <td>Monster Ballads X-Mas</td>\n",
              "      <td>Faster Pussy cat</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SOVFVAK12A8C1350D9</td>\n",
              "      <td>Tanssi vaan</td>\n",
              "      <td>Karkuteillä</td>\n",
              "      <td>Karkkiautomaatti</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SOGTUKN12AB017F4F1</td>\n",
              "      <td>No One Could Ever</td>\n",
              "      <td>Butter</td>\n",
              "      <td>Hudson Mohawke</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SOBNYVR12A8C13558C</td>\n",
              "      <td>Si Vos Querés</td>\n",
              "      <td>De Culo</td>\n",
              "      <td>Yerba Brava</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SOHSBXH12A8C13B0DF</td>\n",
              "      <td>Tangle Of Aspens</td>\n",
              "      <td>Rene Ablaze Presents Winter Sessions</td>\n",
              "      <td>Der Mystic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              song_id              title  ...       artist_name  year\n",
              "0  SOQMMHC12AB0180CB8       Silent Night  ...  Faster Pussy cat  2003\n",
              "1  SOVFVAK12A8C1350D9        Tanssi vaan  ...  Karkkiautomaatti  1995\n",
              "2  SOGTUKN12AB017F4F1  No One Could Ever  ...    Hudson Mohawke  2006\n",
              "3  SOBNYVR12A8C13558C      Si Vos Querés  ...       Yerba Brava  2003\n",
              "4  SOHSBXH12A8C13B0DF   Tangle Of Aspens  ...        Der Mystic     0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Note the problem with repeated track metadata. Let's see of counts play song by users:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(2000000, 3)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user</th>\n",
              "      <th>song</th>\n",
              "      <th>play_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOAKIMP12A8C130995</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBBMDR12A8C13253B</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBXHDL12A81C204C0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBYHAJ12A6701BF1D</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SODACBL12A8C13C273</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       user                song  play_count\n",
              "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOAKIMP12A8C130995           1\n",
              "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBBMDR12A8C13253B           2\n",
              "2  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBXHDL12A81C204C0           1\n",
              "3  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBYHAJ12A6701BF1D           1\n",
              "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SODACBL12A8C13C273           1"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xYdZFAqPv6S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "108f30e2-aebd-45cb-ea39-43b3f1c66e87"
      },
      "source": [
        "unique_track_metadata_df = track_metadata_df.groupby('song_id').max().reset_index()\n",
        "\n",
        "print('Number of rows after unique song Id treatment:', unique_track_metadata_df.shape[0])\n",
        "print('Number of unique songs:', len(unique_track_metadata_df.song_id.unique()))\n",
        "display(unique_track_metadata_df.head())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows after unique song Id treatment: 999056\n",
            "Number of unique songs: 999056\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>song_id</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SOAAABI12A8C13615F</td>\n",
              "      <td>Afro Jazziac</td>\n",
              "      <td>To Birdland And Hurry</td>\n",
              "      <td>Herbie Mann</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SOAAABT12AC46860F0</td>\n",
              "      <td>Herre Gud Ditt Dyre Namn Og Ære</td>\n",
              "      <td>Som Den Gyldne Sol Frembryter</td>\n",
              "      <td>Bergen Big Band</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SOAAABX12A8C13FEB2</td>\n",
              "      <td>N.Y.C. Remix</td>\n",
              "      <td>Paris Can´t Wait</td>\n",
              "      <td>Guardner</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SOAAACR12A58A79456</td>\n",
              "      <td>Irresistible</td>\n",
              "      <td>Wowie Zowie</td>\n",
              "      <td>Superchumbo</td>\n",
              "      <td>2002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SOAAACY12A58A79663</td>\n",
              "      <td>Untitled 1</td>\n",
              "      <td>Pine Cone Temples</td>\n",
              "      <td>Thuja</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              song_id                            title  ...      artist_name  year\n",
              "0  SOAAABI12A8C13615F                     Afro Jazziac  ...      Herbie Mann  2000\n",
              "1  SOAAABT12AC46860F0  Herre Gud Ditt Dyre Namn Og Ære  ...  Bergen Big Band     0\n",
              "2  SOAAABX12A8C13FEB2                     N.Y.C. Remix  ...         Guardner     0\n",
              "3  SOAAACR12A58A79456                     Irresistible  ...      Superchumbo  2002\n",
              "4  SOAAACY12A58A79663                       Untitled 1  ...            Thuja     0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utyg6t3QPv-E"
      },
      "source": [
        "user_song_list_count = pd.merge(count_play_df, \n",
        "                                unique_track_metadata_df, how='left', \n",
        "                                left_on='song', \n",
        "                                right_on='song_id')\n",
        "user_song_list_count.rename(columns={'play_count':'listen_count'},inplace=True)\n",
        "del(user_song_list_count['song_id'])\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ja61gduOQY_t"
      },
      "source": [
        "### Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "pMI85p6SQZQ2",
        "outputId": "b8d07f8d-cca5-4a2d-c70c-1ca6fc381caa"
      },
      "source": [
        "display(user_song_list_count.head())\n",
        "user_song_list_count.listen_count.describe().reset_index().T"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user</th>\n",
              "      <th>song</th>\n",
              "      <th>listen_count</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOAKIMP12A8C130995</td>\n",
              "      <td>1</td>\n",
              "      <td>The Cove</td>\n",
              "      <td>Thicker Than Water</td>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBBMDR12A8C13253B</td>\n",
              "      <td>2</td>\n",
              "      <td>Entre Dos Aguas</td>\n",
              "      <td>Flamenco Para Niños</td>\n",
              "      <td>Paco De Lucia</td>\n",
              "      <td>1976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBXHDL12A81C204C0</td>\n",
              "      <td>1</td>\n",
              "      <td>Stronger</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Kanye West</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBYHAJ12A6701BF1D</td>\n",
              "      <td>1</td>\n",
              "      <td>Constellations</td>\n",
              "      <td>In Between Dreams</td>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SODACBL12A8C13C273</td>\n",
              "      <td>1</td>\n",
              "      <td>Learn To Fly</td>\n",
              "      <td>There Is Nothing Left To Lose</td>\n",
              "      <td>Foo Fighters</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       user  ...  year\n",
              "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...     0\n",
              "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  1976\n",
              "2  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  2007\n",
              "3  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  2005\n",
              "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  1999\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <td>count</td>\n",
              "      <td>mean</td>\n",
              "      <td>std</td>\n",
              "      <td>min</td>\n",
              "      <td>25%</td>\n",
              "      <td>50%</td>\n",
              "      <td>75%</td>\n",
              "      <td>max</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>listen_count</th>\n",
              "      <td>2e+06</td>\n",
              "      <td>3.04548</td>\n",
              "      <td>6.57972</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2213</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  0        1        2    3    4    5    6     7\n",
              "index         count     mean      std  min  25%  50%  75%   max\n",
              "listen_count  2e+06  3.04548  6.57972    1    1    1    3  2213"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "oDoUw3q_QZbX",
        "outputId": "bba7dbcd-ae60-4fe7-fd33-9ecec7f2b7d0"
      },
      "source": [
        "print('{:d} users, {:.2%} of total play counts, listening a single more than 200 times'.format(\n",
        "    count_play_df.user[count_play_df.play_count>200].unique().shape[0],\n",
        "    count_play_df.play_count[count_play_df.play_count>200].count()/count_play_df.shape[0]))\n",
        "display(count_play_df.play_count[count_play_df.play_count>200].describe().reset_index().T)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "118 users, 0.01% of total play counts, listening a single more than 200 times\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <td>count</td>\n",
              "      <td>mean</td>\n",
              "      <td>std</td>\n",
              "      <td>min</td>\n",
              "      <td>25%</td>\n",
              "      <td>50%</td>\n",
              "      <td>75%</td>\n",
              "      <td>max</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>play_count</th>\n",
              "      <td>140</td>\n",
              "      <td>327.107</td>\n",
              "      <td>209.115</td>\n",
              "      <td>201</td>\n",
              "      <td>225</td>\n",
              "      <td>267</td>\n",
              "      <td>353.75</td>\n",
              "      <td>2213</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                0        1        2    3    4    5       6     7\n",
              "index       count     mean      std  min  25%  50%     75%   max\n",
              "play_count    140  327.107  209.115  201  225  267  353.75  2213"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "id": "rjFcCdVfQiDj",
        "outputId": "dabced85-4187-4fc4-9ed1-aaf219521a64"
      },
      "source": [
        "lunatic = count_play_df.play_count[count_play_df.play_count>count_play_df.play_count.max()-10].values[0]\n",
        "print('- How much days the most obsessed user can be the fan of a unique song: {:.1f}'.format((lunatic*3.5)/60/24))\n",
        "display(track_metadata_df[track_metadata_df.song_id.isin(\n",
        "                  count_play_df[count_play_df.play_count>count_play_df.play_count.max()-10].song.values)])\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- How much days the most obsessed user can be the fan of a unique song: 5.4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>song_id</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>567847</th>\n",
              "      <td>SOFCGSE12AF72A674F</td>\n",
              "      <td>Starshine</td>\n",
              "      <td>Gorillaz</td>\n",
              "      <td>Gorillaz</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   song_id      title   release artist_name  year\n",
              "567847  SOFCGSE12AF72A674F  Starshine  Gorillaz    Gorillaz  2000"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "id": "vZqL-miNQlpz",
        "outputId": "49b127fe-bdfd-48f7-c5ef-a6f12c71d238"
      },
      "source": [
        "obsessed = count_play_df.play_count[count_play_df.play_count>count_play_df.play_count.max()-1500].values[1]\n",
        "print('- How much days the second obsessed user can be the fan of a unique song: {:.1f}'.format((obsessed*3)/60/24))\n",
        "display(track_metadata_df[track_metadata_df.song_id.isin(count_play_df[count_play_df.play_count==920].song.values)])\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- How much days the second obsessed user can be the fan of a unique song: 1.9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>song_id</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>927746</th>\n",
              "      <td>SOUAGPQ12A8AE47B3A</td>\n",
              "      <td>Crack Under Pressure</td>\n",
              "      <td>Stress related / Live and learn</td>\n",
              "      <td>Righteous Pigs</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   song_id                 title  ...     artist_name  year\n",
              "927746  SOUAGPQ12A8AE47B3A  Crack Under Pressure  ...  Righteous Pigs  1998\n",
              "\n",
              "[1 rows x 5 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "jwvKSJNDQltz",
        "outputId": "e59bc1f3-4a5d-4757-f454-bddc5b4fe843"
      },
      "source": [
        "fig = plt.figure(figsize=(50, 25))\n",
        "a1 = fig.add_subplot(221)\n",
        "popular_songs = user_song_list_count[['title','listen_count']].groupby('title').sum().\\\n",
        "                sort_values('listen_count', ascending=False).head(20).sort_values('listen_count')\n",
        "f1 = popular_songs.plot(kind='barh', title='Top 20 Most Popular Songs', legend=False, ax=a1)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.ylabel('Item count')\n",
        "\n",
        "a2 = fig.add_subplot(222)\n",
        "popular_release = user_song_list_count[['release','listen_count']].groupby('release').sum().\\\n",
        "                sort_values('listen_count', ascending=False).head(20).sort_values('listen_count')\n",
        "f2 = popular_release.plot(kind='barh', title='Top 20 Most Popular Releases', legend=False, ax=a2)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.ylabel('Item count')\n",
        "\n",
        "a3 = fig.add_subplot(223)\n",
        "popular_artist = user_song_list_count[['artist_name','listen_count']].groupby('artist_name').sum().\\\n",
        "                sort_values('listen_count', ascending=False).head(20).sort_values('listen_count')\n",
        "f1 = popular_artist.plot(kind='barh', title='Top 20 Most Popular Artists', legend=False, ax=a3)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.ylabel('Item count')\n",
        "\n",
        "a4 = fig.add_subplot(224)\n",
        "popular_year = user_song_list_count[['year','listen_count']].groupby('year').sum().\\\n",
        "                sort_values('listen_count', ascending=False).head(20).sort_values('listen_count')\n",
        "f1 = popular_year.plot(kind='barh', title='Top 20 Most Popular Years of Song Listen', legend=False, ax=a4)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.ylabel('Item count')\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 3600x1800 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "oPhPC6Z7QlxM",
        "outputId": "7673f0df-e943-4b7c-8d81-782ec3352d3b"
      },
      "source": [
        "user_song_count_distribution = user_song_list_count[['user','title']].groupby('user').count().\\\n",
        "                                reset_index().sort_values(by='title',ascending = False)\n",
        "\n",
        "def QQ_plot(data, measure):\n",
        "    fig = plt.figure(figsize=(20,7))\n",
        "\n",
        "    #Get the fitted parameters used by the function\n",
        "    (mu, sigma) = norm.fit(data)\n",
        "\n",
        "    #Kernel Density plot\n",
        "    fig1 = fig.add_subplot(121)\n",
        "    sns.distplot(data, fit=norm)\n",
        "    fig1.set_title(measure + ' Distribution ( mu = {:.2f} and sigma = {:.2f} )'.format(mu, sigma), loc='center')\n",
        "    fig1.set_xlabel(measure)\n",
        "    fig1.set_ylabel('Frequency')\n",
        "\n",
        "    #QQ plot\n",
        "    fig2 = fig.add_subplot(122)\n",
        "    res = probplot(data, plot=fig2)\n",
        "    fig2.set_title(measure + ' Probability Plot (skewness: {:.6f} and kurtosis: {:.6f} )'.format(data.skew(), data.kurt()), loc='center')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "QQ_plot(user_song_count_distribution.title, 'Song Title by User')\n",
        "user_song_count_distribution.title.describe().reset_index().T\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <td>count</td>\n",
              "      <td>mean</td>\n",
              "      <td>std</td>\n",
              "      <td>min</td>\n",
              "      <td>25%</td>\n",
              "      <td>50%</td>\n",
              "      <td>75%</td>\n",
              "      <td>max</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>76353</td>\n",
              "      <td>26.1941</td>\n",
              "      <td>31.6251</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>16</td>\n",
              "      <td>31</td>\n",
              "      <td>711</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           0        1        2    3    4    5    6    7\n",
              "index  count     mean      std  min  25%  50%  75%  max\n",
              "title  76353  26.1941  31.6251    1    9   16   31  711"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "ApGxZjsiQl7c",
        "outputId": "9c3cd459-d6a5-4b24-f052-0ebed3b2f3ff"
      },
      "source": [
        "QQ_plot(user_song_list_count[user_song_list_count.title==\"You're The One\"].listen_count, 'Listen Most Popular Song')\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9abLOjpdRNxz"
      },
      "source": [
        "### Recommendation Engine\n",
        "The basis of the recommendation engine is always the recorded interaction between the users and products.\n",
        "\n",
        "**Types of Recommendation Engines**\n",
        "The major area of distinction in different recommendation engines comes from the entity that they assume is the most important in the process of generating recommendations.\n",
        "\n",
        "* User-based recommendation engines: With the user as the central entity, the algorithm will look for similarities among users and on the basis of those similarities will come up with the recommendation.\n",
        "* Content-based recommendation engines: With the content that we are trying to recommend as central entity, the algorithm will attempt to find features about the content and find similar content. Then these similarities will be used to make recommendations to the end users.\n",
        "* Hybrid-recommendation engines: Sometimes termed as collaborative filtering recommendation engines, these are one of the most effective classes of recommendation engines, as they take the best features of both classes, users and content of recommendation engines.\n",
        "\n",
        "**Popularity-Based Recommendation Engine** <br>\n",
        "The most simplest recommendation engine is the Popularity-Based, that basically standing, if some item is liked by a vast majority of our user base, then it is a good idea to recommend that item to users who have not interacted with that item. The code to develop this kind of recommendation is extremely easy and is effectively just a summarization procedure that determine which items of the content have the most users and then that will become our standard recommendation set for each user."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5d61AGgPROD_"
      },
      "source": [
        "def create_popularity_recommendation(train_data, user_id, item_id, n=10):\n",
        "    #Get a count of user_ids for each unique song as recommendation score\n",
        "    train_data_grouped = train_data.groupby([item_id]).agg({user_id: 'count'}).reset_index()\n",
        "    train_data_grouped.rename(columns = {user_id: 'score'},inplace=True)\n",
        "    \n",
        "    #Sort the songs based upon recommendation score\n",
        "    train_data_sort = train_data_grouped.sort_values(['score', item_id], ascending = [0,1])\n",
        "    \n",
        "    #Generate a recommendation rank based upon score\n",
        "    train_data_sort['Rank'] = train_data_sort.score.rank(ascending=0, method='first')\n",
        "        \n",
        "    #Get the top n recommendations\n",
        "    popularity_recommendations = train_data_sort.head(n)\n",
        "    return popularity_recommendations"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "M2aXkjgVRtyN",
        "outputId": "d0a59a07-4688-45dd-ad69-fab07be9abae"
      },
      "source": [
        "recommendations = create_popularity_recommendation(user_song_list_count,'user','title', 15)\n",
        "display(recommendations)\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>score</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6837</th>\n",
              "      <td>Sehr kosmisch</td>\n",
              "      <td>8277</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8726</th>\n",
              "      <td>Undo</td>\n",
              "      <td>7032</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1965</th>\n",
              "      <td>Dog Days Are Over (Radio Edit)</td>\n",
              "      <td>6949</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9497</th>\n",
              "      <td>You're The One</td>\n",
              "      <td>6729</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6499</th>\n",
              "      <td>Revelry</td>\n",
              "      <td>6145</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6826</th>\n",
              "      <td>Secrets</td>\n",
              "      <td>5841</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3438</th>\n",
              "      <td>Horn Concerto No. 4 in E flat K495: II. Romanc...</td>\n",
              "      <td>5385</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2596</th>\n",
              "      <td>Fireflies</td>\n",
              "      <td>4795</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3323</th>\n",
              "      <td>Hey_ Soul Sister</td>\n",
              "      <td>4758</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8495</th>\n",
              "      <td>Tive Sim</td>\n",
              "      <td>4548</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8781</th>\n",
              "      <td>Use Somebody</td>\n",
              "      <td>3976</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5721</th>\n",
              "      <td>OMG</td>\n",
              "      <td>3947</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2120</th>\n",
              "      <td>Drop The World</td>\n",
              "      <td>3879</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5000</th>\n",
              "      <td>Marry Me</td>\n",
              "      <td>3578</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1265</th>\n",
              "      <td>Canada</td>\n",
              "      <td>3526</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  title  score  Rank\n",
              "6837                                      Sehr kosmisch   8277   1.0\n",
              "8726                                               Undo   7032   2.0\n",
              "1965                     Dog Days Are Over (Radio Edit)   6949   3.0\n",
              "9497                                     You're The One   6729   4.0\n",
              "6499                                            Revelry   6145   5.0\n",
              "6826                                            Secrets   5841   6.0\n",
              "3438  Horn Concerto No. 4 in E flat K495: II. Romanc...   5385   7.0\n",
              "2596                                          Fireflies   4795   8.0\n",
              "3323                                   Hey_ Soul Sister   4758   9.0\n",
              "8495                                           Tive Sim   4548  10.0\n",
              "8781                                       Use Somebody   3976  11.0\n",
              "5721                                                OMG   3947  12.0\n",
              "2120                                     Drop The World   3879  13.0\n",
              "5000                                           Marry Me   3578  14.0\n",
              "1265                                             Canada   3526  15.0"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "FIm2DbLxRuCJ",
        "outputId": "a27b0eb0-0ad6-41b0-c945-b5a3fe7f12e5"
      },
      "source": [
        "display(create_popularity_recommendation(user_song_list_count,'user','artist_name', 10))\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>artist_name</th>\n",
              "      <th>score</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>649</th>\n",
              "      <td>Coldplay</td>\n",
              "      <td>29422</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2850</th>\n",
              "      <td>The Black Keys</td>\n",
              "      <td>19862</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1651</th>\n",
              "      <td>Kings Of Leon</td>\n",
              "      <td>18747</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1107</th>\n",
              "      <td>Florence + The Machine</td>\n",
              "      <td>18112</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1370</th>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>17801</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2946</th>\n",
              "      <td>The Killers</td>\n",
              "      <td>16063</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2374</th>\n",
              "      <td>Radiohead</td>\n",
              "      <td>14890</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>736</th>\n",
              "      <td>Daft Punk</td>\n",
              "      <td>14715</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2073</th>\n",
              "      <td>Muse</td>\n",
              "      <td>14005</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1554</th>\n",
              "      <td>Justin Bieber</td>\n",
              "      <td>13959</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 artist_name  score  Rank\n",
              "649                 Coldplay  29422   1.0\n",
              "2850          The Black Keys  19862   2.0\n",
              "1651           Kings Of Leon  18747   3.0\n",
              "1107  Florence + The Machine  18112   4.0\n",
              "1370            Jack Johnson  17801   5.0\n",
              "2946             The Killers  16063   6.0\n",
              "2374               Radiohead  14890   7.0\n",
              "736                Daft Punk  14715   8.0\n",
              "2073                    Muse  14005   9.0\n",
              "1554           Justin Bieber  13959  10.0"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUSdMW9-R9Hu"
      },
      "source": [
        "### Item Similarity Based Recommendation Engine\n",
        "To provide a more personal recommendation to the user we need apply a recommendation engine that consider some kid of similarities between users and theirs items. In other words, it is a recommendation engine based on calculating similarities between a user's items and the other items in our dataset. Usually to define similarity among a set of items, we need a feature set on the basis of which both items can be described. In our case it will mean features of the songs on the basis of which one song can be differentiated from another.\n",
        "\n",
        "Since our dataset don't have this data, we can use the the Jaccard index to do an implicit similarity, based on common users, in terms of the users who listen to these songs. The Jaccard coefficient measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets:imageThe basic idea remains that if two songs are being listened to by a large fraction of common users out of the total listeners, the two songs can be said to be similar to each other.\n",
        "\n",
        "So, we need calculate the similarity of each song in the user's list to those in our dataset, using the similarity metric defined previously, but we need attention to it can become a computation-intensive step when we have a large number of songs. To make the computation more feasible,probably you use a cluster or, in our case, we limited ours items to the most popular 5,000 songs so it is quite unlikely that we would miss out on any important recommendations.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "sqBr33tMRuOt",
        "outputId": "61234291-2f93-4744-94d8-114dc7a0cc91"
      },
      "source": [
        "total_play_count = sum(user_song_list_count.listen_count)\n",
        "play_count = user_song_list_count[['song', 'listen_count']].groupby('song').sum().\\\n",
        "             sort_values(by='listen_count',ascending=False).head(5000)\n",
        "\n",
        "print('5,000 most popular songs represents {:3.2%} of total listen.'.format(float(play_count.sum())/total_play_count))\n",
        "\n",
        "song_subset = list(play_count.index[:5000])\n",
        "user_subset = list(user_song_list_count.loc[user_song_list_count.song.isin(song_subset), 'user'].unique())\n",
        "user_song_list_count_sub = user_song_list_count[user_song_list_count.song.isin(song_subset)]\n",
        "display(user_song_list_count_sub.head())\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5,000 most popular songs represents 81.88% of total listen.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user</th>\n",
              "      <th>song</th>\n",
              "      <th>listen_count</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOAKIMP12A8C130995</td>\n",
              "      <td>1</td>\n",
              "      <td>The Cove</td>\n",
              "      <td>Thicker Than Water</td>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBBMDR12A8C13253B</td>\n",
              "      <td>2</td>\n",
              "      <td>Entre Dos Aguas</td>\n",
              "      <td>Flamenco Para Niños</td>\n",
              "      <td>Paco De Lucia</td>\n",
              "      <td>1976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBXHDL12A81C204C0</td>\n",
              "      <td>1</td>\n",
              "      <td>Stronger</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Kanye West</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBYHAJ12A6701BF1D</td>\n",
              "      <td>1</td>\n",
              "      <td>Constellations</td>\n",
              "      <td>In Between Dreams</td>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SODACBL12A8C13C273</td>\n",
              "      <td>1</td>\n",
              "      <td>Learn To Fly</td>\n",
              "      <td>There Is Nothing Left To Lose</td>\n",
              "      <td>Foo Fighters</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       user  ...  year\n",
              "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...     0\n",
              "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  1976\n",
              "2  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  2007\n",
              "3  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  2005\n",
              "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  1999\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "rOylgwLzRuTx",
        "outputId": "0c6ef03f-11d9-40d8-f964-475b51c1dc92"
      },
      "source": [
        "is_model = item_similarity_recommender_py()\n",
        "is_model.create(user_song_list_count_sub, 'user', 'title')\n",
        "user_id = list(user_song_list_count_sub.user)[7]\n",
        "user_items = is_model.get_user_items(user_id)\n",
        "\n",
        "#Recommend songs for the user using personalized model\n",
        "is_model.recommend(user_id)\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of unique songs for the user: 33\n",
            "no. of unique songs in the training set: 4867\n",
            "Non zero values in cooccurence_matrix :120655\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>song</th>\n",
              "      <th>score</th>\n",
              "      <th>rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>Quiet Houses</td>\n",
              "      <td>0.044710</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>Meadowlarks</td>\n",
              "      <td>0.043836</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>Heard Them Stirring</td>\n",
              "      <td>0.042740</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>Tiger Mountain Peasant Song</td>\n",
              "      <td>0.041485</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>Sun It Rises</td>\n",
              "      <td>0.040973</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>Your Protector</td>\n",
              "      <td>0.039942</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>Oliver James</td>\n",
              "      <td>0.039287</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>Great Indoors</td>\n",
              "      <td>0.036765</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>White Winter Hymnal</td>\n",
              "      <td>0.036345</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>If I Could</td>\n",
              "      <td>0.034576</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    user_id  ... rank\n",
              "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    1\n",
              "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    2\n",
              "2  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    3\n",
              "3  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    4\n",
              "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    5\n",
              "5  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    6\n",
              "6  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    7\n",
              "7  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    8\n",
              "8  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    9\n",
              "9  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...   10\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "qxevwtTVSIyg",
        "outputId": "9ede42b3-8197-466b-bdfd-f1511e7d1062"
      },
      "source": [
        "display(user_song_list_count_sub[(user_song_list_count_sub.user==user_id) & (user_song_list_count_sub.listen_count>5)])\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user</th>\n",
              "      <th>song</th>\n",
              "      <th>listen_count</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOMGIYR12AB0187973</td>\n",
              "      <td>6</td>\n",
              "      <td>Behind The Sea [Live In Chicago]</td>\n",
              "      <td>Live In Chicago</td>\n",
              "      <td>Panic At The Disco</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOYHEPA12A8C13097F</td>\n",
              "      <td>8</td>\n",
              "      <td>Moonshine</td>\n",
              "      <td>Thicker Than Water</td>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        user  ...  year\n",
              "16  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...     0\n",
              "43  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  2003\n",
              "\n",
              "[2 rows x 7 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwrYfe8sSP1W"
      },
      "source": [
        "As you can see, although the algorithms seem to have done a good job of customizing the recommendations, it is clear that we can improve them, so we go to the next recommendation system a bit more complex.\n",
        "\n",
        "### Matrix factorization based recommendations\n",
        "Matrix factorization based recommendation engines) are probably the most used recommendation engines when it comes to implementing recommendation engines in production.\n",
        "\n",
        "Matrix factorization are methods that reduce a matrix into constituent parts, such that when these matrices are multiplied we get the original matrix. It make it easier to calculate more complex matrix operations. Matrix factorization methods, also called matrix decompositions methods, are a foundation of linear algebra in computers, even for basic operations such as solving systems of linear equations, calculating the inverse, and calculating the determinant of a matrix.\n",
        "\n",
        "Matrix factorization can be used to discover latent features between two different kinds of entities. For example, we can try to explain a song in mathematical terms by measuring its beats, tempo, and other such features and then define similar features in terms of the user. Once we have consciously defined such \"features\", we can use them to find matches for a user based on some similarity criteria. You can use matrix factorization to discover these latent features and they seem to work great.\n",
        "\n",
        "The starting point of any matrix factorization-based method is the utility matrix, a matrix of user Vs item dimension. Not, this is a sparse matrix, since not all item is used by the user. The process of matrix factorization means finding out a low rank approximation of the utility matrix. So we want to break down the utility matrix U into two low rank matrices so that we can recreate the matrix U by multiplying those two matrices:\n",
        "\n",
        "Assuming the process helps us identify latent factors/features, meaning as K, our aim is to find two matrices X and Y such that their product (matrix multiplication) approximates R.\n",
        "\n",
        "X = |U| x K matrix (A matrix with dimensions of num_users * factors)\n",
        "\n",
        "Y = |P| x K matrix (A matrix with dimensions of factors * num_songs)\n",
        "\n",
        "To make a recommendation to the user, we can multiply the corresponding user's row from the first matrix by the item matrix and determine the items from the row with maximum ratings. That will become our recommendations for the user. The first matrix represents the association between the users and the latent features, while the second matrix takes care of the associations between items (songs in our case) and the latent features.\n",
        "\n",
        "Matrix Factorization and Singular Value Decomposition (SVD)\n",
        "There are multiple algorithms available for determining factorization of any matrix. We use one of the simplest algorithms, which is the singular value decomposition or SVD. You can follow these steps to determine the factorization of a matrix using the output of SVD function.\n",
        "\n",
        "Factorize the matrix to obtain U, S, and V matrices.\n",
        "Reduce the matrix S to first k components. (The function we are using will only provide k dimensions, so we can skip this step.)\n",
        "Compute the square root of reduced matrix Sk to obtain the matrix Sk1/2.\n",
        "Compute the two resultant matrix U*Sk1/2 and Sk1/2*V as these will serve as our two factorized matrices\n",
        "We can then generate the prediction of user i for product j by taking the dot product of the ith row of the first matrix with the jth column of the second matrix.\n",
        "\n",
        "Let's create tree functions to help us on it:\n",
        "\n",
        "* compute_svd: It use the svds function provided by the scipy library to break down our utility matrix into three different matrices, and prosed with the others tree steps above.\n",
        "* compute_estimated_matrix: use the decomposed matrices by SVD and provide the predictions.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WeCF-0eSI3n"
      },
      "source": [
        "def compute_svd(urm, K):\n",
        "    U, s, Vt = svds(urm, K)\n",
        "\n",
        "    dim = (len(s), len(s))\n",
        "    S = np.zeros(dim, dtype=np.float32)\n",
        "    for i in range(0, len(s)):\n",
        "        S[i,i] = mt.sqrt(s[i])\n",
        "\n",
        "    U = csc_matrix(U, dtype=np.float32)\n",
        "    S = csc_matrix(S, dtype=np.float32)\n",
        "    Vt = csc_matrix(Vt, dtype=np.float32)\n",
        "    \n",
        "    return U, S, Vt\n",
        "\n",
        "def compute_estimated_matrix(urm, U, S, Vt, uTest, K, test):\n",
        "    rightTerm = S*Vt \n",
        "    max_recommendation = 250\n",
        "    estimatedRatings = np.zeros(shape=(MAX_UID, MAX_PID), dtype=np.float16)\n",
        "    recomendRatings = np.zeros(shape=(MAX_UID,max_recommendation ), dtype=np.float16)\n",
        "    for userTest in uTest:\n",
        "        prod = U[userTest, :]*rightTerm\n",
        "        estimatedRatings[userTest, :] = prod.todense()\n",
        "        recomendRatings[userTest, :] = (-estimatedRatings[userTest, :]).argsort()[:max_recommendation]\n",
        "    return recomendRatings\n",
        "\n",
        "def show_recomendations(uTest, num_recomendations = 10):\n",
        "    for user in uTest:\n",
        "        print('-'*70)\n",
        "        print(\"Recommendation for user id {}\".format(user))\n",
        "        rank_value = 1\n",
        "        i = 0\n",
        "        while (rank_value <  num_recomendations + 1):\n",
        "            so = uTest_recommended_items[user,i:i+1][0]\n",
        "            if (small_set.user[(small_set.so_index_value == so) & (small_set.us_index_value == user)].count()==0):\n",
        "                song_details = small_set[(small_set.so_index_value == so)].\\\n",
        "                    drop_duplicates('so_index_value')[['title','artist_name']]\n",
        "                print(\"The number {} recommended song is {} BY {}\".format(rank_value, \n",
        "                                                                      list(song_details['title'])[0],\n",
        "                                                                      list(song_details['artist_name'])[0]))\n",
        "                rank_value+=1\n",
        "            i += 1\n"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUyA4fixSpyw"
      },
      "source": [
        "The first thing that we notice is that we have no concept of \"rating\" in our data. All we have are the play counts of various songs. This is a well known problem in the case of recommendation engines and is called the \"implicit feedback\" problem. There are many ways to solve this problem but we will look at a very simple and intuitive solution. We will replace the play count with a fractional play count. The logic being that this will measure the strength of \"likeness\" for a song in the range of [0,1].\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "OIIpzfW1SI7F",
        "outputId": "75cc2e97-3b75-4255-eda7-500c9f5fc8a3"
      },
      "source": [
        "user_song_list_listen = user_song_list_count[['user','listen_count']].groupby('user').sum().reset_index()\n",
        "user_song_list_listen.rename(columns={'listen_count':'total_listen_count'},inplace=True)\n",
        "user_song_list_count_merged = pd.merge(user_song_list_count,user_song_list_listen)\n",
        "user_song_list_count_merged['fractional_play_count'] = \\\n",
        "    user_song_list_count_merged['listen_count']/user_song_list_count_merged['total_listen_count']\n",
        "\n",
        "display(user_song_list_count_merged[user_song_list_count_merged.user =='d6589314c0a9bcbca4fee0c93b14bc402363afea']\\\n",
        "        [['user','song','listen_count','fractional_play_count']].head())\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user</th>\n",
              "      <th>song</th>\n",
              "      <th>listen_count</th>\n",
              "      <th>fractional_play_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
              "      <td>SOADQPP12A67020C82</td>\n",
              "      <td>12</td>\n",
              "      <td>0.038961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
              "      <td>SOAFTRR12AF72A8D4D</td>\n",
              "      <td>1</td>\n",
              "      <td>0.003247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
              "      <td>SOANQFY12AB0183239</td>\n",
              "      <td>1</td>\n",
              "      <td>0.003247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
              "      <td>SOAYATB12A6701FD50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.003247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>303</th>\n",
              "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
              "      <td>SOBOAFP12A8C131F36</td>\n",
              "      <td>7</td>\n",
              "      <td>0.022727</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         user  ... fractional_play_count\n",
              "299  d6589314c0a9bcbca4fee0c93b14bc402363afea  ...              0.038961\n",
              "300  d6589314c0a9bcbca4fee0c93b14bc402363afea  ...              0.003247\n",
              "301  d6589314c0a9bcbca4fee0c93b14bc402363afea  ...              0.003247\n",
              "302  d6589314c0a9bcbca4fee0c93b14bc402363afea  ...              0.003247\n",
              "303  d6589314c0a9bcbca4fee0c93b14bc402363afea  ...              0.022727\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBvsPa_iSvmj"
      },
      "source": [
        "The next transformation of data that is required is to convert our dataframe into a numpy matrix in the format of utility matrix. We will convert our dataframe into a sparse matrix, as we will have a lot of missing values and sparse matrices are suitable for representation of such a matrix. Since we won't be able to transform our song IDs and user IDs into a numpy matrix, we will convert these indices into numerical indices. Then we will use these transformed indices to create our sparse numpy matrix.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "id": "sXYfUzCxSJB7",
        "outputId": "6ff7de87-aee4-4b5e-92cf-a17366280c9a"
      },
      "source": [
        "user_codes = user_song_list_count_merged.user.drop_duplicates().reset_index()\n",
        "user_codes.rename(columns={'index':'user_index'}, inplace=True)\n",
        "user_codes['us_index_value'] = list(user_codes.index)\n",
        "\n",
        "song_codes = user_song_list_count_merged.song.drop_duplicates().reset_index()\n",
        "song_codes.rename(columns={'index':'song_index'}, inplace=True)\n",
        "song_codes['so_index_value'] = list(song_codes.index)\n",
        "\n",
        "small_set = pd.merge(user_song_list_count_merged,song_codes,how='left')\n",
        "small_set = pd.merge(small_set,user_codes,how='left')\n",
        "mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']]\n",
        "\n",
        "data_array = mat_candidate.fractional_play_count.values\n",
        "row_array = mat_candidate.us_index_value.values\n",
        "col_array = mat_candidate.so_index_value.values\n",
        "\n",
        "data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float)\n",
        "\n",
        "display(data_sparse)\n",
        "\n",
        "print('User used to performed the item similarity based recommendations earlie:.')\n",
        "display(user_codes[user_codes.user =='b80344d063b5ccb3212f76538f3d9e43d87dca9e'])\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<76353x10000 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 2000000 stored elements in COOrdinate format>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "User used to performed the item similarity based recommendations earlie:.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_index</th>\n",
              "      <th>user</th>\n",
              "      <th>us_index_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_index                                      user  us_index_value\n",
              "0           0  b80344d063b5ccb3212f76538f3d9e43d87dca9e               0"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWsGWwR9Sz9H",
        "outputId": "73d7c83f-b251-4af6-e00a-6f9c332d92b0"
      },
      "source": [
        "K=50\n",
        "urm = data_sparse\n",
        "MAX_PID = urm.shape[1]\n",
        "MAX_UID = urm.shape[0]\n",
        "\n",
        "U, S, Vt = compute_svd(urm, K)\n",
        "uTest = [4,5,6,7,8,873,23]\n",
        "\n",
        "uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt, uTest, K, True)\n",
        "\n",
        "show_recomendations(uTest)\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------\n",
            "Recommendation for user id 4\n",
            "The number 1 recommended song is 911 BY Wyclef Jean / Mary J. Blige\n",
            "The number 2 recommended song is If I Ain't Got You BY Alicia Keys\n",
            "The number 3 recommended song is Sample Track 2 BY Simon Harris\n",
            "The number 4 recommended song is Kennedy Rag BY Suzy Thompson\n",
            "The number 5 recommended song is Clocks BY Coldplay\n",
            "The number 6 recommended song is Yellow BY Coldplay\n",
            "The number 7 recommended song is Hey_ Soul Sister BY Train\n",
            "The number 8 recommended song is The Scientist BY Coldplay\n",
            "The number 9 recommended song is Eco BY Jorge Drexler\n",
            "The number 10 recommended song is Kryptonite BY 3 Doors Down\n",
            "----------------------------------------------------------------------\n",
            "Recommendation for user id 5\n",
            "The number 1 recommended song is Eenie Meenie BY Sean Kingston and Justin Bieber\n",
            "The number 2 recommended song is Sample Track 2 BY Simon Harris\n",
            "The number 3 recommended song is U Smile BY Justin Bieber\n",
            "The number 4 recommended song is Love Me BY Justin Bieber\n",
            "The number 5 recommended song is One Time BY Justin Bieber\n",
            "The number 6 recommended song is That Should Be Me BY Justin Bieber\n",
            "The number 7 recommended song is Teach Me How To Dougie BY California Swag District\n",
            "The number 8 recommended song is The Gift BY Angels and Airwaves\n",
            "The number 9 recommended song is Kennedy Rag BY Suzy Thompson\n",
            "The number 10 recommended song is One Less Lonely Girl BY Justin Bieber\n",
            "----------------------------------------------------------------------\n",
            "Recommendation for user id 6\n",
            "The number 1 recommended song is Pursuit Of Happiness (nightmare) BY Kid Cudi / MGMT / Ratatat\n",
            "The number 2 recommended song is The Scientist BY Coldplay\n",
            "The number 3 recommended song is Lucky (Album Version) BY Jason Mraz & Colbie Caillat\n",
            "The number 4 recommended song is Vanilla Twilight BY Owl City\n",
            "The number 5 recommended song is Bleed It Out [Live At Milton Keynes] BY Linkin Park\n",
            "The number 6 recommended song is The Only Exception (Album Version) BY Paramore\n",
            "The number 7 recommended song is All The Right Moves BY OneRepublic\n",
            "The number 8 recommended song is Forever BY Drake / Kanye West / Lil Wayne / Eminem\n",
            "The number 9 recommended song is Love Story BY Taylor Swift\n",
            "The number 10 recommended song is Heartbreak Warfare BY John Mayer\n",
            "----------------------------------------------------------------------\n",
            "Recommendation for user id 7\n",
            "The number 1 recommended song is Lucky (Album Version) BY Jason Mraz & Colbie Caillat\n",
            "The number 2 recommended song is Fix You BY Coldplay\n",
            "The number 3 recommended song is Use Somebody BY Kings Of Leon\n",
            "The number 4 recommended song is Shiver BY Coldplay\n",
            "The number 5 recommended song is Don't Panic BY Coldplay\n",
            "The number 6 recommended song is Heartbreak Warfare BY John Mayer\n",
            "The number 7 recommended song is Sparks BY Coldplay\n",
            "The number 8 recommended song is Pursuit Of Happiness (nightmare) BY Kid Cudi / MGMT / Ratatat\n",
            "The number 9 recommended song is Uprising BY Muse\n",
            "The number 10 recommended song is Bitter Sweet Symphony BY The Verve\n",
            "----------------------------------------------------------------------\n",
            "Recommendation for user id 8\n",
            "The number 1 recommended song is Alejandro BY Lady GaGa\n",
            "The number 2 recommended song is Dog Days Are Over (Radio Edit) BY Florence + The Machine\n",
            "The number 3 recommended song is Just Dance BY Lady GaGa / Colby O'Donis\n",
            "The number 4 recommended song is Billionaire [feat. Bruno Mars]  (Explicit Album Version) BY Travie McCoy\n",
            "The number 5 recommended song is Sample Track 2 BY Simon Harris\n",
            "The number 6 recommended song is OMG BY Usher featuring will.i.am\n",
            "The number 7 recommended song is Greece 2000 BY Three Drives\n",
            "The number 8 recommended song is Clocks BY Coldplay\n",
            "The number 9 recommended song is I Gotta Feeling BY Black Eyed Peas\n",
            "The number 10 recommended song is Livin' On A Prayer BY Bon Jovi\n",
            "----------------------------------------------------------------------\n",
            "Recommendation for user id 873\n",
            "The number 1 recommended song is Dead Souls (LP Version) BY Nine Inch Nails\n",
            "The number 2 recommended song is Canada BY Five Iron Frenzy\n",
            "The number 3 recommended song is Behind The Sea [Live In Chicago] BY Panic At The Disco\n",
            "The number 4 recommended song is Sample Track 2 BY Simon Harris\n",
            "The number 5 recommended song is Creep (Explicit) BY Radiohead\n",
            "The number 6 recommended song is Home BY Edward Sharpe & The Magnetic Zeros\n",
            "The number 7 recommended song is Kryptonite BY 3 Doors Down\n",
            "The number 8 recommended song is Without Me BY Eminem\n",
            "The number 9 recommended song is Paradise City BY Guns N' Roses\n",
            "The number 10 recommended song is Livin' On A Prayer BY Bon Jovi\n",
            "----------------------------------------------------------------------\n",
            "Recommendation for user id 23\n",
            "The number 1 recommended song is Pursuit Of Happiness (nightmare) BY Kid Cudi / MGMT / Ratatat\n",
            "The number 2 recommended song is Day 'N' Nite BY Kid Cudi Vs Crookers\n",
            "The number 3 recommended song is I Gotta Feeling BY Black Eyed Peas\n",
            "The number 4 recommended song is Imma Be BY Black Eyed Peas\n",
            "The number 5 recommended song is Teach Me How To Dougie BY California Swag District\n",
            "The number 6 recommended song is Sexy Bitch BY DJ Dizzy\n",
            "The number 7 recommended song is Use Somebody BY Kings Of Leon\n",
            "The number 8 recommended song is Yeah! BY Usher Featuring Lil' Jon & Ludacris\n",
            "The number 9 recommended song is Nothin' On You [feat. Bruno Mars] (Album Version) BY B.o.B\n",
            "The number 10 recommended song is Bulletproof BY La Roux\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AjJu4JsVS2ZI",
        "outputId": "dc0cc05a-fd3f-4f85-a475-3dfeee9208dd"
      },
      "source": [
        "uTest = [0]\n",
        "#Get estimated rating for test user\n",
        "print(\"Predictied ratings:\")\n",
        "uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt, uTest, K, True)\n",
        "show_recomendations(uTest)\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictied ratings:\n",
            "----------------------------------------------------------------------\n",
            "Recommendation for user id 0\n",
            "The number 1 recommended song is Basic Space BY The xx\n",
            "The number 2 recommended song is Infinity BY The xx\n",
            "The number 3 recommended song is I Wanna Be Sedated (Remastered Album Version ) BY Ramones\n",
            "The number 4 recommended song is Fantasy BY The xx\n",
            "The number 5 recommended song is The Big Gundown BY The Prodigy\n",
            "The number 6 recommended song is CIEÑ BY Milczenie Owiec\n",
            "The number 7 recommended song is Home BY Edward Sharpe & The Magnetic Zeros\n",
            "The number 8 recommended song is Cosmic Love BY Florence + The Machine\n",
            "The number 9 recommended song is Speak BY Godsmack\n",
            "The number 10 recommended song is You've Got The Love BY Florence + The Machine\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9A18KJJTWMyf"
      },
      "source": [
        ""
      ],
      "execution_count": 38,
      "outputs": []
    }
  ]
}