CF_knn_music_recommender.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting python-Levenshtein\n",
      "  Downloading python_Levenshtein-0.21.0-py3-none-any.whl (9.4 kB)\n",
      "Collecting Levenshtein==0.21.0\n",
      "  Downloading Levenshtein-0.21.0-cp39-cp39-win_amd64.whl (101 kB)\n",
      "     -------------------------------------- 101.0/101.0 kB 6.1 MB/s eta 0:00:00\n",
      "Collecting rapidfuzz<4.0.0,>=2.3.0\n",
      "  Downloading rapidfuzz-3.0.0-cp39-cp39-win_amd64.whl (1.8 MB)\n",
      "     ---------------------------------------- 1.8/1.8 MB 9.6 MB/s eta 0:00:00\n",
      "Installing collected packages: rapidfuzz, Levenshtein, python-Levenshtein\n",
      "Successfully installed Levenshtein-0.21.0 python-Levenshtein-0.21.0 rapidfuzz-3.0.0\n",
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 23.1.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install python-Levenshtein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from recommeders.knn_recommender import Recommender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read userid-songid-listen_count\n",
    "song_info = pd.read_csv('10000.txt',sep='\\t',header=None)\n",
    "song_info.columns = ['user_id', 'song_id', 'listen_count']\n",
    "\n",
    "#Read song  metadata\n",
    "song_actual =  pd.read_csv('song_data.csv')\n",
    "song_actual.drop_duplicates(['song_id'], inplace=True)\n",
    "\n",
    "#Merge the two dataframes above to create input dataframe for recommender systems\n",
    "songs = pd.merge(song_info, song_actual, on=\"song_id\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>user_id</th>\n",
       "      <th>song_id</th>\n",
       "      <th>listen_count</th>\n",
       "      <th>title</th>\n",
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       "      <td>Learn To Fly</td>\n",
       "      <td>There Is Nothing Left To Lose</td>\n",
       "      <td>Foo Fighters</td>\n",
       "      <td>1999</td>\n",
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      "text/plain": [
       "                                    user_id             song_id  listen_count  \\\n",
       "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOAKIMP12A8C130995             1   \n",
       "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBBMDR12A8C13253B             2   \n",
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       "3  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBYHAJ12A6701BF1D             1   \n",
       "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SODACBL12A8C13C273             1   \n",
       "\n",
       "             title                        release    artist_name  year  \n",
       "0         The Cove             Thicker Than Water   Jack Johnson     0  \n",
       "1  Entre Dos Aguas            Flamenco Para Niños  Paco De Lucia  1976  \n",
       "2         Stronger                     Graduation     Kanye West  2007  \n",
       "3   Constellations              In Between Dreams   Jack Johnson  2005  \n",
       "4     Learn To Fly  There Is Nothing Left To Lose   Foo Fighters  1999  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs.to_csv('songs.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_songs = pd.read_csv('songs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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      "text/plain": [
       "                                    user_id             song_id  listen_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   \n",
       "\n",
       "             title                        release    artist_name  year  \n",
       "0         The Cove             Thicker Than Water   Jack Johnson     0  \n",
       "1  Entre Dos Aguas            Flamenco Para Niños  Paco De Lucia  1976  \n",
       "2         Stronger                     Graduation     Kanye West  2007  \n",
       "3   Constellations              In Between Dreams   Jack Johnson  2005  \n",
       "4     Learn To Fly  There Is Nothing Left To Lose   Foo Fighters  1999  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_songs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id         0\n",
       "song_id         0\n",
       "listen_count    0\n",
       "title           0\n",
       "release         0\n",
       "artist_name     0\n",
       "year            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_songs.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id         object\n",
       "song_id         object\n",
       "listen_count     int64\n",
       "title           object\n",
       "release         object\n",
       "artist_name     object\n",
       "year             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_songs.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9567 unique songs in the dataset\n"
     ]
    }
   ],
   "source": [
    "#Unique songs\n",
    "unique_songs = df_songs['title'].unique().shape[0]\n",
    "print(f\"There are {unique_songs} unique songs in the dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3375 unique artists in the dataset\n"
     ]
    }
   ],
   "source": [
    "#Unique artists\n",
    "unique_artists = df_songs['artist_name'].unique().shape[0]\n",
    "print(f\"There are {unique_artists} unique artists in the dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 76353 unique users in the dataset\n"
     ]
    }
   ],
   "source": [
    "#Unique users\n",
    "unique_users = df_songs['user_id'].unique().shape[0]\n",
    "print(f\"There are {unique_users} unique users in the dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#count how many rows we have by song, we show only the ten more popular songs \n",
    "ten_pop_songs = df_songs.groupby('title')['listen_count'].count().reset_index().sort_values(['listen_count', 'title'], ascending = [0,1])\n",
    "ten_pop_songs['percentage']  = round(ten_pop_songs['listen_count'].div(ten_pop_songs['listen_count'].sum())*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>6836</th>\n",
       "      <td>Sehr kosmisch</td>\n",
       "      <td>8277</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8725</th>\n",
       "      <td>Undo</td>\n",
       "      <td>7032</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>Dog Days Are Over (Radio Edit)</td>\n",
       "      <td>6949</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9496</th>\n",
       "      <td>You're The One</td>\n",
       "      <td>6729</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6498</th>\n",
       "      <td>Revelry</td>\n",
       "      <td>6145</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6825</th>\n",
       "      <td>Secrets</td>\n",
       "      <td>5841</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3437</th>\n",
       "      <td>Horn Concerto No. 4 in E flat K495: II. Romanc...</td>\n",
       "      <td>5385</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2595</th>\n",
       "      <td>Fireflies</td>\n",
       "      <td>4795</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3322</th>\n",
       "      <td>Hey_ Soul Sister</td>\n",
       "      <td>4758</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8494</th>\n",
       "      <td>Tive Sim</td>\n",
       "      <td>4548</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title  listen_count  \\\n",
       "6836                                      Sehr kosmisch          8277   \n",
       "8725                                               Undo          7032   \n",
       "1964                     Dog Days Are Over (Radio Edit)          6949   \n",
       "9496                                     You're The One          6729   \n",
       "6498                                            Revelry          6145   \n",
       "6825                                            Secrets          5841   \n",
       "3437  Horn Concerto No. 4 in E flat K495: II. Romanc...          5385   \n",
       "2595                                          Fireflies          4795   \n",
       "3322                                   Hey_ Soul Sister          4758   \n",
       "8494                                           Tive Sim          4548   \n",
       "\n",
       "      percentage  \n",
       "6836        0.41  \n",
       "8725        0.35  \n",
       "1964        0.35  \n",
       "9496        0.34  \n",
       "6498        0.31  \n",
       "6825        0.29  \n",
       "3437        0.27  \n",
       "2595        0.24  \n",
       "3322        0.24  \n",
       "8494        0.23  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ten_pop_songs = ten_pop_songs[:10]\n",
    "ten_pop_songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ten_pop_songs['title'].tolist()\n",
    "counts = ten_pop_songs['listen_count'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.barplot(x=counts, y=labels, palette='Set3')\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count how many rows we have by artist name, we show only the ten more popular artist \n",
    "ten_pop_artists  = df_songs.groupby(['artist_name'])['listen_count'].count().reset_index().sort_values(['listen_count', 'artist_name'], \n",
    "                                                                                                ascending = [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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>listen_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>Coldplay</td>\n",
       "      <td>29422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>The Black Keys</td>\n",
       "      <td>19862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>Kings Of Leon</td>\n",
       "      <td>18747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>Florence + The Machine</td>\n",
       "      <td>18112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>Jack Johnson</td>\n",
       "      <td>17801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>The Killers</td>\n",
       "      <td>16063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>Radiohead</td>\n",
       "      <td>14890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>Daft Punk</td>\n",
       "      <td>14715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>Muse</td>\n",
       "      <td>14005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>Justin Bieber</td>\n",
       "      <td>13959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 artist_name  listen_count\n",
       "649                 Coldplay         29422\n",
       "2850          The Black Keys         19862\n",
       "1651           Kings Of Leon         18747\n",
       "1107  Florence + The Machine         18112\n",
       "1370            Jack Johnson         17801\n",
       "2946             The Killers         16063\n",
       "2374               Radiohead         14890\n",
       "736                Daft Punk         14715\n",
       "2073                    Muse         14005\n",
       "1554           Justin Bieber         13959"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ten_pop_artists = ten_pop_artists[:10]\n",
    "ten_pop_artists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "labels = ten_pop_artists['artist_name'].tolist()\n",
    "counts = ten_pop_artists['listen_count'].tolist()\n",
    "sns.barplot(x=counts, y=labels, palette='Set2')\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "listen_counts = pd.DataFrame(df_songs.groupby('listen_count').size(), columns=['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum time the same user listened to the same songs was: 2213\n"
     ]
    }
   ],
   "source": [
    "print(f\"The maximum time the same user listened to the same songs was: {listen_counts.reset_index(drop=False)['listen_count'].iloc[-1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On average, a user listen to the same song 3.0454845 times\n"
     ]
    }
   ],
   "source": [
    "print(f\"On average, a user listen to the same song {df_songs['listen_count'].mean()} times\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "sns.boxplot(x='listen_count', data=df_songs)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "listen_counts_temp = listen_counts[listen_counts['count'] > 50].reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AuiDAAgAA0AUBFgAAgC5sNcBW1XlVdWNV3VRVL5moe3xVfaaqvm6b8wEAAKBfWwuwVXVMklcneXKS05M8u6pOH6n74SRXbmsuAAAA9G+b78CeleSm1toHWmt/m+QNSc4fqHthkl9L8tEtzgUAAIDObTPAnpTkloXz++eX3amqTkry9CQXTw1UVRdU1buq6l233XbbYZ8oAAAAu982A2wNXNaWzv9Eku9trX1maqDW2iWttTNba2fu3bv3cM0PAACAjuzZ4tj7kzxs4fzJSW5dqjkzyRuqKklOSPKUqrqjtXbZFucFAABAh7YZYK9OcmpVPSLJnyZ5VpJ/tljQWnvEgdNV9bokbxZeAQAAGLK1ANtau6OqXpDZpwsfk+TS1tr1VfX8+fWTf/cKAAAAi7b5Dmxaa1ckuWLpssHg2lr71m3OBQAAgL5t80OcAAAA4LARYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAsCLAAAAF0QYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAsCLAAAAF0QYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAsCLAAAAF0QYAEAAOjCnqM9gSPh059+29qa44479wjMBAAAgEPlHVgAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAsCLAAAAF0QYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAsCLAAAAF0QYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAsCLAAAAF0QYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAsCLAAAAF0QYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBQEWAACALgiwAAAAdGGrAbaqzquqG6vqpqp6ycD131hV182//qCqHrfN+QAAANCvrQXYqjomyauTPDnJ6UmeXVWnL5V9MMlXttYem+TlSS7Z1nwAAADo2zbfgT0ryU2ttQ+01v42yRuSnL9Y0Fr7g9baJ+Zn35Hk5C3OBwAAgI5tM8CelOSWhfP755eNeW6S/7rF+QAAANCxPVscuwYua4OFVU/MLMB+xcj1FyS5IEke/vCHH675AQAA0JFtvgO7P8nDFs6fnOTW5aKqemyS1yY5v7X2saGBWmuXtNbObK2duXfv3q1MFgAAgN1tmwH26iSnVtUjqurYJM9KcvliQVU9PMkbk3xza+39W5wLAAAAndvaIcSttTuq6gVJrkxyTJJLW2vXV9Xz59dfnOQHknxekp+pqiS5o7V25rbmBAAAQL+2+Tewaa1dkeSKpcsuXjj9vCTP2+YcAAAAuGfY5iHEAAAAcNgIsAAAAHRBgAUAAKALAiwAAABdEGABAADoggALAABAFwRYAAAAuiDAAgAA0AUBFgAAgC4IsAAAAHRBgAUAAKALAiwAAABdEGABAADoggALAABAFwRYAAAAuiDAAgAA0IU9R3sCu81Vv/O+tTVnP/HRR2AmAAAALPIOLAAAAF0QYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAsCLAAAAF0QYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAsCLAAAAF3Yc7QncG9y0WXvXFvzPU876wjMBAAAoD/egQUAAKALAiwAAABdEGABAADogr+BvZtu+/F/v7Zm73d9/xGYCQAAwD2bd2ABAADoggALAABAFwRYAAAAuiDAAgAA0AUBFgAAgC4IsAAAAHRBgAUAAKALAiwAAABdEGABAADoggALAABAFwRYAAAAuiDAAgAA0IU9R3sCjLvtzS9eW7P3a37kCMwEAADg6PMOLAAAAF0QYAEAAOiCAAsAAEAXBFgAAAC6IMACAADQBZ9CfA9x2TUvXVvztDNecQRmAgAAsB3egQUAAKALAiwAAABdcAjxvdCn9926Ud1xj3nolmcCAACwOQGWw+qKH75io7qnfO9TtjwTAADgnkaAZa33v//9a2tOO+20IzATAADg3kyA5ai65sNXr60548THH4GZAAAAu91WA2xVnZfkVUmOSfLa1torl66v+fVPSfKpJN/aWnvPNudEvz7+J/9ubc3nfuEPHIGZAAAAR8PWAmxVHZPk1Un+SZL9Sa6uqstbazcslD05yanzry9N8pr5d7jbXnP1VWtrvv3xZx/S2Ff9zvvW1pz9xEcf0tgAAMCwbb4De1aSm1prH0iSqnpDkvOTLAbY85O8vrXWkryjqo6vqhNbax/e4rxgxac//ba1Nccdd+4hjX3bj//7tTV7v+v7kyQXXfbOjcb8nqeddUhzAQCAnm0zwJ6U5JaF8/uz+u7qUM1JSQRY2MBtb37x2pq9X/MjSZLLrnnpRmM+7YxXJNns3y0d+FdLO/2gr00+rfrAJ1Xv9O+kd3Ko+U7epd/kRY7krhc6dvIu/SYvciR3vdABAHBvVbM3P7cwcNUzk3x1a+158/PfnOSs1toLF2p+I8krWmv///z825K8uLX27qWxLkhywfzsI5PcOHCTJyS5fcPp7aTW2Lt3HsY+smPvlnkYe/fOw9i7dx7G3r3zMPbunYexd+88jL1753G4xr69tXbe4E+01rbyleQfJrly4fxLk7x0qeY/JXn2wvkbk5x4iLf3rm3UGnv3zsPY+m7s3TUPY+/eeRh7987D2Lt3HsbevfMw9u6dx7bHbq3lPtmeq5OcWlWPqKpjkzwryeVLNZcn+Zaa+bIkf9H8/SsAAAADtvY3sK21O6rqBUmuzOzf6FzaWru+qp4/v/7iJFdk9i90bsrs3+g8Z1vzAQAAoG9b/T+wrbUrMgupi5ddvHC6JfmOw3Rzl2yp1ti7dx7GPrJj75Z5GHv3zsPYu3cext698zD27p2HsXfvPIy9e+ex7bG39yFOAAAAcDht829gAQAA4PDZ6ac+7bavJJcm+WiSfRvUPizJ7yR5X5Lrk3znRO39krwzybXz2pdtOJ9jkvxRkjevqbs5yXuTXJMNPn0ryfFJfjXJf5/P/x+O1D1yPuaBr08medHEuN81v3/7kvxykvutmcd3zmuvHxp3qB9JPjfJbyb54/n3B0/UPnM+9t8lOXODsS+aPybXJXlTkuMnal8+r7smyVuTPHTd71CS707SkpywZh4XJvnThcf9KVNjJ3lhZp+6fX2SH1kz9v+7MO7NSa6ZqD0jyTsO/F5l9q+rpsZ+XJK3Z/a7+OtJHjS1VoZ6OVE72MuJ+pVeTtSO9XJyjS/2c2LslV5OjTvUy4mxV3o5UTvYy4n6lV5mZDs21Mep7d5QLydqx9bkWP1KL8dqR/o4Nu5KH9dt25d7OTH22Jocq1/p5UTt4Jocen4Z6+NE/dT2dbl2sI8T9YNrcqh2avs6MvZgL8fGXu7jmrEHezlSu9LHidqpPt6cpef/qV6O1I9tX4dqR3s5Uj+2fV2pHevlyLhTfRwce6iXI2NP9XGo/owMb1+Haqd6eXyW9s0yvn0dqp1ak0P1Y9vXodqxPq7UTq3JkbEvzPD2dXDsjO/zDI09tn0dqh3s40T90PPk4H5zxvs4Vj/0PDlWO7S/M1Y71sfJ/f0c/Dw5NvaFGe7j6NhZfZ4cG3tqTa5kj7HHe+xrbSDb7V9Jzk7yxdkswJ6Y5Ivnpx+Y5P1JTh+prSQPmJ++b5I/TPJlG9zGv07yS9kswJ6wbryF+v8nyfPmp4/N0s7EyM8ck+QjSb5w5PqTknwwyXHz87+S5FsnxnvM/Jft/pn9/fRvJTl1XT/mv+AvmZ9+SZIfnqh99Hwx/G5WN+ZD9V+VZM/89A+vGXvxSedfJbl46ncos6BwZZI/ycEb86GxL0zy3Zv8fiZ54vyx+6z5+Yds+vuc5MeS/MDE2G9N8uT56ack+d01c7k6yVfOT39bkpdPrZWhXk7UDvZyon6llxO1Y70cXePL/ZwYe6WXE7WDvZyax3IvJ8Ye7OVE/UovM7IdG+rj/PRY/UovJ2rH1uRY/Uovx2pH+jg27kof18xjpZdT8xhZk2Njr/RyonZwTc7PH/T8MtbHifqp7ety7WAfJ+oH1+RQ7dT2dWTswV6O1I5uX8fmMtTLkbGntq/LtVN9vHngPo/2cqR+bPs6VDvay5H6se3rSu1YL0fGnerjUP3Y9nVwHhN9HBp7bPs6VDvVy5V9s7FejtROrcmh+rHt61DtWB8H9yeH+jgx9mAvR2qn9nkm921z8PZ1aOypNTlUP9rL+WV37jeP9XGifrSXA7Xrtq+LtaPb1qH6qV4OjD3Yx4n6ddvXwdyx1MfB7LHJ47341f0hxK21q5J8fMPaD7fW3jM//ZeZvSJz0khta6391fzsfedfbWr8qjo5yT9N8trNZr+ZqnpQZuHjZ+dz+9vW2p9v8KPnJvkfrbU/majZk+S4qtqTWTC9daL20Une0Vr7VGvtjiS/l+TpiwUj/Tg/sw1J5t+fNlbbWntfa+3GoRsfqX/rfC7J7FW4kydqP7lw9rMz7+fE79CPJ3lxlvq+w9+5odpvT/LK1trfzGs+usnYVVVJvj6zV6vGaltmrygmyedkoZ8j9Y9MctX89G8m+dp57dhaWenlWO1YLyfqV3o5UTvWy6k1flA/d7g9GKsd7OW6sRd7OVE72MuJ+pVeTmzHxtbkYP1QLydqx9bkWP1KL9dsf5f7uKNt9UT9Si/XjT2wJsfqV3o5UTu4JkeeXwb7OFY/tiZHagf7OFE/uCYnnhcHt687eR4dqR3dvk6NvdzLkdrBNTlSO9jHCaO9HDL1XDlQO9rLkfrBXk4Y7OVhMNrLMct9nDD6XDlgbE2O7Zut9HKsdmJNjtWv9HKidqWPa/YnV/q4k/3PidrBPq4be7GXE7Vja3Ksft26XNxv3mRN3lm/wZpcrF23JhdrN1mPy/v7U2tyk2wwVr9uTa6MPbImh7LHjraB3QfYQ1VVpyT5B5m96j1Wc0xVXZPZYZe/2VobrZ37icx+Yf5ugym0JG+tqndX1QVrav9ektuS/FxV/VFVvbaqPnuD23hWJjbirbU/TfKjST6U5MOZ/R/et06Mty/J2VX1eVV1/9x1aOU6n9/m/993/v0hG/zMofi2JP91qqCqfqiqbknyjZm9+zVW99Qkf9pau3YHt/+Cqrquqi6tqgdP1J2W5AlV9YdV9XtV9fgNx39Ckj9rrf3xRM2Lklw0v48/muSla8bcl+Sp89PPzEA/l9bKZC83WVcb1q/0crl2XS8X69f1c2Aeo71cql3by5H7ONjLpdoXZU0vl+oHezmyHRvt4062exvUHtTHsfqhXg7VjvVxYh6DfRypH+zlmvu40seR+hdloJcjtWNr8iey+vwytR6H6sesq11ej4P1I2typXbNehyby1Avh2qn1uTU/Vzu5VDtizK8Jodqp7atQ8//U73cyf7CutrlXg7Wj/RypXail2PzGNu2DtWP9XLqPg5tW4fqX5ThXg7VjvVybN9sqJc73Y/bpP5AL0drB/o4WDvRx6l5LPdyrHasj+vu42Ivx2pflOE+jtWv2+dZ3G/eZN91cj97w9qhfdeDakfW42D9mu3r0DzW7bsu1q/b5xm6jwetyYnssbOs0Cbenu3lK8kp2eAQ4oX6ByR5d5JnbFh/fGZ/c/aYiZqvSfIz89PnZP0hxAeOYX9IZn8DdfZE7ZlJ7kjypfPzr8rSYQ8DP3NsktvnvxBjNQ9O8ttJ9mb26v9lSb5pzbjPTfKezF7BujjJj6/rR5I/X7r+E+t6l/FDMMbqvy+zvyOoTX4vMtvIvWyoNrNXg/4wyefMz9+c1UMwlu/j52d26MR9kvxQZv/3eKx2X5KfzOwQwrMyO5Ri7byTvCbJv1kzj5/M7J23ZPaK12+tqX9UZofgvDvJDyb52NRaWdPLwXU10cux+qFejq7Z5V4u16/r58B9nOrlcu26Xo7dx6FeLo+9rpfL9et6eXzm27GpPg7Vb9DLodqVPk7VT/TyQO1jp/o4cB9H+zhSv66XQ/dxpY8jY6/r5WLtSh8z8vwy1sex+qE+blB7UB/X1S/2cag2E+tx4n6u9HKidrCPG9zPO3s5MfZKHydqR9djBp7/x3o5Vj+2JtfUDm1bJ/dFsrAmR+Y91suh2qlt61D9WC+n7uPQtnVo7ME1OVI72MuM7JsN9XKsdqKP6+rv7OW62qU1OVR70UQfx+7j0Jocqx3r47r7uLgmx8Ye6+NY/dS6PGi/eaiPS+cH97OXe7mmdmhNju6/Z/g58s76rN/fWb6Pk8+TA/Wjz5MT9/GgNZmR7LHu8V55LKau7OUrOwiw8wfryiT/eoe38YOZOE48ySuS7J//snwkyaeS/MKGY1+4ZuwvSHLzwvknJPmNNWOen+Sta2qemeRnF85/S+ZPxBvO+z8k+Zfr+pHZH3ufOD99YpIb1/VuaAMwVp/kn2f2R/n33/T3IrNj+fcN1Sb5PzN7R+Tm+dcdmb1S9AUbjr18/5fPvyXJOQvn/0eSvWvu454kf5bZYbVTt/UXuWtjUkk+uYPH5LQk75xaK2O9HKqd6uVY/VAvp8Ye6eVB9VP93GDsxd+LocdjtJcT93GllyNjj/Zyg3kf1MuFy38wsw92GF2TQ/Xr1uVy7VAf14091Mul2n871scNxj1laNylx2RyXQ7cx8E1OTL25LqcmPdpmX3Q0+Dzy1gfx+qH+jhVO9THdWMv9nGk9tfG+rjh2KdMjP0LY31ccz8P6uXE2Ct93HDOg+txft2F2dmavDCbr8k7a4d6uW7sNWvywmy+JofGPWVo3KXHZJM1uXgfN1mTB8beZE0OzfvOXmZk32yol2O1Y32cql/u5bqxl9bkUO3bxvq44dinTIz9G2N9XHMfl9fk2NiDfdxw3sv7POdnYb95qI9LP39Q/dSaHKpd7uO6ccfW42J91uy/rhn7lKmx5+en9nmG7uPQ/s5g9lj3eC9/3asOIa6qyux4+Pe11v7jmtq9VXX8/PRxSf5xZp8YNqi19tLW2smttVMyewv9t1tr3zQy9mdX1QMPnM7sj7n3TYz9kSS3VNUj5xedm+SGqfkneXbWH9bwoSRfVlX3nz8252b293Sjquoh8+8Pz+zdrU0Onbg8s4Wa+ff/ssHPbKSqzkvyvUme2lr71JraUxfOPjUj/Wytvbe19pDW2inzfu7P7ENzPjIx9okLZ5+eiX5m9mrTk+Y/d1ruetVqyj9O8t9ba/vX1N2a5Cvnp5+U2ae5jVro532SfH9m76pPrZWVXu5kXU2NPdTLidrBXg7Vj/Uzs43q0NgrvZy4j5dloJdrHpODejlRO9jLicdkpZcT27HBNbmT7d5Y7dianKhf6eVI7R+N9PEzI+MOrsmJ+3hZVnuZicdjZU1OjL3Sy4nHY6WPE88vg33cyfPRWO1YHyfqV/o4Uvu1Y9vXibFXejlxH4f6ePuax+SgXk7UrvRxYs5j29ax5/+xNbnx/sJY7cSaHKsfWpNDtVePrMm/HBl3bE2O3cehXn5q4vEYWpNjYw+tybHHY7CXE/tmK73c6X7cWP1QLydqh9bkUO17Jtbk2NhDa3LsPl6W4TU59Zgsr8mx2sHnyYl5D/Zybnm/ed2+6yb72YO1a/Zdl2vX7bveWb/B/uvy2Ov2XZfv42UZ338dejyG9l3HssfOssJUuu3ha/5gfTjJ/5o36rkTtV+R2d83XJeBj3Ffqn1sZh+Jf928oT+wgzmdk4lDiDM7Nv/a3PXvE75vgzHPyOwjwq+b/wKNfrx0ZocQfCzzQwjWjPuyzBbDviQ/n/kni03U/35mG41rk5y7ST+SfF5mr/D98fz7507UPn1++m8yCxhXrhn7piS3LPTz4onaX5vfz+sy+/j0kzb5HcrqIRhDY/98Zh/Lfl1mi/DEidpjM3s1f19mh2M/ad3vc5LXJXn+Bo/1V2R2aMy1mR1G8iVr6r8zs0+xfX+SV+auVzIH18pQLydqB3s5Ub/Sy4nasV6uXeMH+jkx9kovJ2oHezk1jyz1cmLswV5O1K/0MiPbsYyvybH6lV5O1I6tybH6lV6O1Y70cWzcsTU5Vr/Sy6l5LPdxzdgrvZyoHVyTQ88vY32cqB/dvg7UDvZxon5wTa57XszS9nVk7MFejtSObl/H5jLUy5GxR7evA7Vj29bB5/+xXk7UD63JsdqxNTlWP7Qm1+635K41OTbu2Jocqx9ak6PzGOrjxNhDa3KsdnRNZmDfbKKXQ7VT+zxD9WO9HKode55cqV2zzzM09lgvh2qn9nkG5zLSy6Gxp/Z5hurH1uXKfvNYHyfqx/Z5hmrH+jhUO7ptHaof6+XI2KPb1pH6sX2ewXkM9XF++Ur2mHq8h74ONA4AAAB2tXvVIcQAAAD0S4AFAACgCwIsAAAAXRBgAQAA6IIACwAAQBcEWAAAALogwALAhqrqr+bfH1pVvzpRd3xV/csjN7PDo6rOqaovP9rzAIAxAiwA7FBr7dbW2tdNlByfpLsAm+ScJAIsALuWAAsAO1RVp1TVvvnp/6Oq3llV11TVdVV1apJXJvmi+WUXzeu+p6qunte8bGGc91XVf66q66vqrVV13MTt/v2q+q2quraq3lNVX1QzF1XVvqp6b1V9w7z2nKp688LP/nRVfev89M1V9bL5GO+tqkdV1SlJnp/ku+bzfsJ2Hj0AOHR7jvYEAKBzz0/yqtbaL1bVsUmOSfKSJI9prZ2RJFX1VUlOTXJWkkpyeVWdneRD88uf3Vr7F1X1K0m+NskvjNzWLyZ5ZWvtTVV1v8xeiH5GkjOSPC7JCUmurqqrNpj37a21L54f6vzdrbXnVdXFSf6qtfajO38YAGD7BFgAuHvenuT7qurkJG9srf1xVS3XfNX864/m5x+QWXD9UJIPttaumV/+7iSnDN1IVT0wyUmttTclSWvtf84v/4okv9xa+0ySP6uq30vy+CSfXDPvNy7c5jPW300AOPocQgwAd0Nr7ZeSPDXJp5NcWVVPGiirJK9orZ0x//r7rbWfnV/3Nwt1n8n4i8srqXjN5Xfk4Of5+y1df+B2p24TAHYVARYA7oaq+ntJPtBa+8kklyd5bJK/TPLAhbIrk3xbVT1g/jMnVdVDdnI7rbVPJtlfVU+bj/FZVXX/JFcl+YaqOqaq9iY5O8k7k/xJktPndZ+T5NwNbmZ53gCwqwiwAHD3fEOSfVV1TZJHJXl9a+1jSf7b/IOVLmqtvTXJLyV5e1W9N8mv5tCC4jcn+VdVdV2SP0jyBUnelOS6JNcm+e0kL26tfaS1dkuSX5lf94u56/DlKb+e5Ok+xAmA3apaa0d7DgAAALCWd2ABAADogg9tAIBdpqpeneQfLV38qtbazx2N+QDAbuEQYgAAALrgEGIAAAC6IMACAADQBQEWAACALgiwAAAAdEGABQAAoAv/G74LgA9OiGqlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "sns.barplot(x='listen_count', y='count', palette='Set3', data=listen_counts_temp)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "song_user = df_songs.groupby('user_id')['song_id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "sns.distplot(song_user.values, color='orange')\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A user listens to an average of 26.194124657839247 songs\n"
     ]
    }
   ],
   "source": [
    "print(f\"A user listens to an average of {np.mean(song_user)} songs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A user listens to an average of 16.0 songs, with minimum 1 and maximum 711 songs\n"
     ]
    }
   ],
   "source": [
    "print(f\"A user listens to an average of {np.median(song_user)} songs, with minimum {np.min(song_user)} and maximum {np.max(song_user)} songs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get how many values should it be if all songs have been listen by all users\n",
    "values_matrix = unique_users * unique_songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substract the total values with the actural shape of the DataFrame songs\n",
    "zero_values_matrix = values_matrix - df_songs.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The matrix of users x songs has 728469151 values that are zero\n"
     ]
    }
   ],
   "source": [
    "print(f\"The matrix of users x songs has {zero_values_matrix} values that are zero\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get users which have listen to at least 16 songs\n",
    "song_ten_id = song_user[song_user > 16].index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtered the dataset to keep only those users with more than 16 listened\n",
    "df_song_id_more_ten = df_songs[df_songs['user_id'].isin(song_ten_id)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the dataframe into a pivot table\n",
    "df_songs_features = df_song_id_more_ten.pivot(index='song_id', columns='user_id', values='listen_count').fillna(0)\n",
    "\n",
    "# obtain a sparse matrix\n",
    "mat_songs_features = csr_matrix(df_songs_features.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "user_id             000e2c2a8c7870ff9121f212b35c8b3a20cc0e67  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
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       "user_id             000ebc858861aca26bac9b49f650ed424cf882fc  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
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       "user_id             000ef25cc955ad5841c915d269432eea41f4a1a5  \\\n",
       "song_id                                                        \n",
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       "user_id             0012bf75d43a724f62dc746d9e85ae0088a3a1d6  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
       "SOAACPJ12A81C21360                                       0.0   \n",
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       "SOAAEJI12AB0188AB5                                       0.0   \n",
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       "\n",
       "user_id             001322829b5dc3edc59bf78189617ddd8f23c82a  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
       "SOAACPJ12A81C21360                                       0.0   \n",
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       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
       "SOAACPJ12A81C21360                                       0.0   \n",
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       "\n",
       "user_id             0019740e3e8c24e223a6f88e3faa7c144ec5a014  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
       "SOAACPJ12A81C21360                                       0.0   \n",
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       "user_id             001b005fe5e80d3cb45f320f5658fc8e2e72794e  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
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       "user_id             001f22c638730aed5659034c447d3cf0e658898e  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
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       "SOAAEJI12AB0188AB5                                       0.0   \n",
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       "\n",
       "user_id             0021d9a4628624f6d70237f9c200ab82e766bf26  ...  \\\n",
       "song_id                                                       ...   \n",
       "SOAAAGQ12A8C1420C8                                       0.0  ...   \n",
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       "\n",
       "user_id             fff300cd094fe04030b79fae550dc9d065190182  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
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       "\n",
       "user_id             fff4676dacb2e9a7217702f62ee70e88aa512ecc  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
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       "song_id                                                        \n",
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       "\n",
       "user_id             fff543db7918cb8f4f56f7470903eb2f1d5a6dd8  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
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       "SOAAEJI12AB0188AB5                                       0.0   \n",
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       "\n",
       "user_id             fff6c30c773e6ffafcac213c9afd9666afaf6d63  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
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       "\n",
       "user_id             fffb701ee87a32eff67eb040ed59146121f01571  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
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       "\n",
       "user_id             fffc0df75a48d823ad5abfaf2a1ee61eb1e3302c  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
       "SOAACPJ12A81C21360                                       0.0   \n",
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       "SOAAEJI12AB0188AB5                                       0.0   \n",
       "SOAAFAC12A67ADF7EB                                       0.0   \n",
       "\n",
       "user_id             fffce9c1537fbc350ea68823d956eaa8f5236dbe  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
       "SOAACPJ12A81C21360                                       0.0   \n",
       "SOAACSG12AB018DC80                                       0.0   \n",
       "SOAAEJI12AB0188AB5                                       0.0   \n",
       "SOAAFAC12A67ADF7EB                                       0.0   \n",
       "\n",
       "user_id             fffd9635b33f412de8ed02e44e6564e3644cf3c6  \\\n",
       "song_id                                                        \n",
       "SOAAAGQ12A8C1420C8                                       0.0   \n",
       "SOAACPJ12A81C21360                                       0.0   \n",
       "SOAACSG12AB018DC80                                       0.0   \n",
       "SOAAEJI12AB0188AB5                                       0.0   \n",
       "SOAAFAC12A67ADF7EB                                       0.0   \n",
       "\n",
       "user_id             fffea3d509760c984e7d40789804c0e5e289cc86  \n",
       "song_id                                                       \n",
       "SOAAAGQ12A8C1420C8                                       0.0  \n",
       "SOAACPJ12A81C21360                                       0.0  \n",
       "SOAACSG12AB018DC80                                       0.0  \n",
       "SOAAEJI12AB0188AB5                                       0.0  \n",
       "SOAAFAC12A67ADF7EB                                       0.0  \n",
       "\n",
       "[5 rows x 36561 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_songs_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_unique_songs = df_songs.drop_duplicates(subset=['song_id']).reset_index(drop=True)[['song_id', 'title']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "decode_id_song = {\n",
    "    song: i for i, song in \n",
    "    enumerate(list(df_unique_songs.set_index('song_id').loc[df_songs_features.index].title))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Recommender(metric='cosine', algorithm='brute', k=20, data=mat_songs_features, decode_id_song=decode_id_song)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "song = 'I believe in miracles'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting the recommendation process for I believe in miracles ...\n",
      "... Done\n"
     ]
    }
   ],
   "source": [
    "new_recommendations = model.make_recommendation(new_song=song, n_recommendations=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The recommendations for I believe in miracles are:\n",
      "['Nine Million Bicycles', 'If You Were A Sailboat', 'Shy Boy', 'I Cried For You', \"Spider's Web\", 'Piece By Piece', 'On The Road Again', 'Blues In The Night', 'Blue Shoes', 'Thank You Stars']\n"
     ]
    }
   ],
   "source": [
    "print(f\"The recommendations for {song} are:\")\n",
    "print(f\"{new_recommendations}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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