BPR_Music_RecSys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e08f25dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.sparse as sparse\n",
    "from scipy.sparse.linalg import spsolve\n",
    "import random\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "import implicit\n",
    "\n",
    "# Load the data\n",
    "raw_data = pd.read_table('data/usersha1-artmbid-artname-plays.tsv')\n",
    "raw_data = raw_data.drop(raw_data.columns[1], axis=1)\n",
    "raw_data.columns = ['user', 'artist', 'plays']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52d78ae2",
   "metadata": {},
   "outputs": [
    {
     "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>artist</th>\n",
       "      <th>plays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00000c289a1829a808ac09c00daf10bc3c4e223b</td>\n",
       "      <td>die Ärzte</td>\n",
       "      <td>1099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00000c289a1829a808ac09c00daf10bc3c4e223b</td>\n",
       "      <td>melissa etheridge</td>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00000c289a1829a808ac09c00daf10bc3c4e223b</td>\n",
       "      <td>elvenking</td>\n",
       "      <td>717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00000c289a1829a808ac09c00daf10bc3c4e223b</td>\n",
       "      <td>juliette &amp; the licks</td>\n",
       "      <td>706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00000c289a1829a808ac09c00daf10bc3c4e223b</td>\n",
       "      <td>red hot chili peppers</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       user                 artist  plays\n",
       "0  00000c289a1829a808ac09c00daf10bc3c4e223b              die Ärzte   1099\n",
       "1  00000c289a1829a808ac09c00daf10bc3c4e223b      melissa etheridge    897\n",
       "2  00000c289a1829a808ac09c00daf10bc3c4e223b              elvenking    717\n",
       "3  00000c289a1829a808ac09c00daf10bc3c4e223b   juliette & the licks    706\n",
       "4  00000c289a1829a808ac09c00daf10bc3c4e223b  red hot chili peppers    691"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2bd5fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data1 = raw_data[0:2000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58320658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of users in the data: 40913\n",
      "Total number of artists in the data: 110820\n"
     ]
    }
   ],
   "source": [
    "print(f'Total number of users in the data: {raw_data1.user.nunique()}')\n",
    "print(f'Total number of artists in the data: {raw_data1.artist.nunique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ffcd2158",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw_data1.dropna()\n",
    "data = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79cc77d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['user'] = data['user'].astype('category')\n",
    "data['artist'] = data['artist'].astype('category')\n",
    "data['user_id'] = data['user'].cat.codes\n",
    "data['artist_id'] = data['artist'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5912ec13",
   "metadata": {},
   "outputs": [
    {
     "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>user</th>\n",
       "      <th>artist</th>\n",
       "      <th>plays</th>\n",
       "      <th>user_id</th>\n",
       "      <th>artist_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00000c289a1829a808ac09c00daf10bc3c4e223b</td>\n",
       "      <td>die Ärzte</td>\n",
       "      <td>1099</td>\n",
       "      <td>0</td>\n",
       "      <td>30264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00000c289a1829a808ac09c00daf10bc3c4e223b</td>\n",
       "      <td>melissa etheridge</td>\n",
       "      <td>897</td>\n",
       "      <td>0</td>\n",
       "      <td>66512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       user             artist  plays  \\\n",
       "0  00000c289a1829a808ac09c00daf10bc3c4e223b          die Ärzte   1099   \n",
       "1  00000c289a1829a808ac09c00daf10bc3c4e223b  melissa etheridge    897   \n",
       "\n",
       "   user_id  artist_id  \n",
       "0        0      30264  \n",
       "1        0      66512  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c90e21b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_item_user = sparse.csr_matrix((data['plays'].astype(float), (data['artist_id'], data['user_id'])))\n",
    "sparse_user_item = sparse.csr_matrix((data['plays'].astype(float), (data['user_id'], data['artist_id'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3ec92ab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<110820x40913 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 1999967 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_item_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "165220e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<40913x110820 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 1999967 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_user_item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a6a0a966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.95588936009682"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_size = sparse_user_item.shape[0]*sparse_user_item.shape[1]\n",
    "num_purchases = len(sparse_user_item.nonzero()[0])\n",
    "sparsity = 100*(1 - (num_purchases / matrix_size))\n",
    "sparsity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f2eb765a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def make_train(ratings, pct_test = 0.2):\n",
    "    \n",
    "    test_set = ratings.copy() # Make a copy of the original set to be the test set. \n",
    "    test_set[test_set != 0] = 1 # Store the test set as a binary preference matrix\n",
    "    \n",
    "    training_set = ratings.copy() # Make a copy of the original data we can alter as our training set. \n",
    "    \n",
    "    nonzero_inds = training_set.nonzero() # Find the indices in the ratings data where an interaction exists\n",
    "    nonzero_pairs = list(zip(nonzero_inds[0], nonzero_inds[1])) # Zip these pairs together of item,user index into list\n",
    "\n",
    "    \n",
    "    random.seed(0) # Set the random seed to zero for reproducibility\n",
    "    \n",
    "    num_samples = int(np.ceil(pct_test*len(nonzero_pairs))) # Round the number of samples needed to the nearest integer\n",
    "    samples = random.sample(nonzero_pairs, num_samples) # Sample a random number of item-user pairs without replacement\n",
    "\n",
    "    item_inds = [index[0] for index in samples] # Get the item row indices\n",
    "\n",
    "    user_inds = [index[1] for index in samples] # Get the user column indices\n",
    "\n",
    "    \n",
    "    training_set[item_inds, user_inds] = 0 # Assign all of the randomly chosen user-item pairs to zero\n",
    "    training_set.eliminate_zeros() # Get rid of zeros in sparse array storage after update to save space\n",
    "    \n",
    "    return training_set, test_set, list(set(user_inds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2f0c5ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "product_train, product_test, product_users_altered = make_train(sparse_item_user, pct_test = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "772579e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Intel MKL BLAS detected. Its highly recommend to set the environment variable 'export MKL_NUM_THREADS=1' to disable its internal multithreading\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1578cae65f7441e4a76cf9c5cad9092b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/40 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = implicit.als.AlternatingLeastSquares(factors=20, regularization=0.1, iterations=40)\n",
    "\n",
    "# Calculate the confidence by multiplying it by our alpha value.(alpha value corresponds to the confidence metric \n",
    "# that we discussed earlier)\n",
    "\n",
    "alpha_val = 15\n",
    "data_conf = (product_train * alpha_val).astype('double')\n",
    "\n",
    "# We have used an alpha_val of 15 after performing some iterations with different alpha values\n",
    "#Fit the model\n",
    "model.fit(data_conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6d849627",
   "metadata": {},
   "outputs": [],
   "source": [
    "item_vecs = model.item_factors\n",
    "user_vecs = model.user_factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ade4155c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the artist vector matrix: (110820, 20)\n",
      "Shape of the user vector matrix: (40913, 20)\n"
     ]
    }
   ],
   "source": [
    "print(f'Shape of the artist vector matrix: {item_vecs.shape}')\n",
    "print(f'Shape of the user vector matrix: {user_vecs.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "71645861",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pylab as plt\n",
    "def auc_score(predictions, test):\n",
    "    '''\n",
    "    This simple function will output the area under the curve using sklearn's metrics. \n",
    "    \n",
    "    parameters:\n",
    "    \n",
    "    - predictions: your prediction output\n",
    "    \n",
    "    - test: the actual target result you are comparing to\n",
    "    \n",
    "    returns:\n",
    "    \n",
    "    - AUC (area under the Receiver Operating Characterisic curve)\n",
    "    '''\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(test, predictions)\n",
    "    return metrics.auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "df97a4b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_mean_auc(training_set, altered_users, predictions, test_set):\n",
    "    \n",
    "    store_auc = [] # An empty list to store the AUC for each user that had an item removed from the training set\n",
    "    popularity_auc = [] # To store popular AUC scores\n",
    "    pop_items = np.array(test_set.sum(axis = 1)).reshape(-1) # Get sum of item iteractions to find most popular\n",
    "    item_vecs = predictions[1]\n",
    "    for user in altered_users: # Iterate through each user that had an item altered\n",
    "        training_column = training_set[:,user].toarray().reshape(-1) # Get the training set column\n",
    "        zero_inds = np.where(training_column == 0) # Find where the interaction had not yet occurred\n",
    "        \n",
    "        # Get the predicted values based on our user/item vectors\n",
    "        user_vec = predictions[0][user,:]\n",
    "        pred = user_vec.dot(item_vecs).toarray()[0,zero_inds].reshape(-1)\n",
    "        \n",
    "        # Get only the items that were originally zero\n",
    "        # Select all ratings from the MF prediction for this user that originally had no iteraction\n",
    "        actual = test_set[:,user].toarray()[zero_inds,0].reshape(-1)\n",
    "        \n",
    "        # Select the binarized yes/no interaction pairs from the original full data\n",
    "        # that align with the same pairs in training \n",
    "        pop = pop_items[zero_inds] # Get the item popularity for our chosen items\n",
    "        \n",
    "        store_auc.append(auc_score(pred, actual)) # Calculate AUC for the given user and store\n",
    "        \n",
    "        popularity_auc.append(auc_score(pop, actual)) # Calculate AUC using most popular and score\n",
    "    # End users iteration\n",
    "    \n",
    "    return float('%.3f'%np.mean(store_auc)), float('%.3f'%np.mean(popularity_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0532d2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.962, 0.934)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_mean_auc(product_train, product_users_altered,\n",
    "              [sparse.csr_matrix(user_vecs), sparse.csr_matrix(item_vecs.T)], product_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c6fd8a4c",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <th>plays</th>\n",
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       "      <td>72</td>\n",
       "      <td>40910</td>\n",
       "      <td>80876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5693 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             user                 artist  \\\n",
       "4        00000c289a1829a808ac09c00daf10bc3c4e223b  red hot chili peppers   \n",
       "1422     000429493d9716b66b02180d208d09b5b89fbe64  red hot chili peppers   \n",
       "2139     0007e26aafcfc0b6dcb87d7041583fbb7cced88a  red hot chili peppers   \n",
       "3284     000b0bb32f149504e1df3cce85b6bfd20cef3dd0  red hot chili peppers   \n",
       "3322     000b2ee840cbda56e0f41c8f248c4fb7ee275db3  red hot chili peppers   \n",
       "...                                           ...                    ...   \n",
       "1998983  1d2c0053f7e585caa02ccbb3a96b708c078e9300  red hot chili peppers   \n",
       "1999418  1d2d697dbe47814d1de4064508b32ec00a1ec767  red hot chili peppers   \n",
       "1999642  1d2db9f945283c3f48ab685a84a78429a63a3f19  red hot chili peppers   \n",
       "1999823  1d2e1fa030318413f194f741c674b6032094a0ce  red hot chili peppers   \n",
       "1999889  1d2e583e63cbd9cfc526b53f90f4fd438271336e  red hot chili peppers   \n",
       "\n",
       "         plays  user_id  artist_id  \n",
       "4          691        0      80876  \n",
       "1422       234       29      80876  \n",
       "2139       159       44      80876  \n",
       "3284        46       68      80876  \n",
       "3322        87       69      80876  \n",
       "...        ...      ...        ...  \n",
       "1998983   1024    40892      80876  \n",
       "1999418    384    40901      80876  \n",
       "1999642     66    40905      80876  \n",
       "1999823    389    40909      80876  \n",
       "1999889     72    40910      80876  \n",
       "\n",
       "[5693 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['artist'] == 'red hot chili peppers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2e4294bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "red hot chili peppers\n",
      "muse\n",
      "nirvana\n",
      "coldplay\n",
      "queen\n",
      "the killers\n",
      "foo fighters\n",
      "placebo\n",
      "pink floyd\n",
      "the beatles\n"
     ]
    }
   ],
   "source": [
    "artist_id = 80876\n",
    "n_similar = 10 # getting the top ten similar items\n",
    "\n",
    "# Use implicit to get similar items.\n",
    "similar = model.similar_items(artist_id, n_similar)\n",
    "# Print the names of our most similar artists\n",
    "for artist in similar:\n",
    "    idx, score = artist\n",
    "    print (data.artist.loc[data.artist_id == idx].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0d37cf46",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "                                           user     artist  plays  user_id  \\\n",
       "0      00000c289a1829a808ac09c00daf10bc3c4e223b  die Ärzte   1099        0   \n",
       "2943   000a1585c5f65532a9c9187a882892982d345a5c  die Ärzte    148       61   \n",
       "3787   000cb6427411006fe9a6193d3c4f59efed53fbef  die Ärzte      7       78   \n",
       "6295   0014ffc91d3a5b59cce9bceaf22ef0d72e5711b8  die Ärzte     88      128   \n",
       "13513  003059a886782e4d7936da913d3f064f637d0b2b  die Ärzte      5      274   \n",
       "\n",
       "       artist_id  \n",
       "0          30264  \n",
       "2943       30264  \n",
       "3787       30264  \n",
       "6295       30264  \n",
       "13513      30264  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['artist'] == 'die Ärzte'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "edb749cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "die Ärzte\n",
      "guano apes\n",
      "mando diao\n",
      "soundtrack\n",
      "apocalyptica\n",
      "him\n",
      "limp bizkit\n",
      "[unknown]\n",
      "bloodhound gang\n",
      "nightwish\n"
     ]
    }
   ],
   "source": [
    "# Find the 10 most similar to die Ärzte\n",
    "artist_id = 30264\n",
    "n_similar = 10 # getting the top ten similar items\n",
    "\n",
    "# Use implicit to get similar items.\n",
    "similar = model.similar_items(artist_id, n_similar)\n",
    "# Print the names of our most similar artists\n",
    "for artist in similar:\n",
    "    idx, score = artist\n",
    "    print (data.artist.loc[data.artist_id == idx].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "076e43ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['rank'] = data.groupby(['user_id'])['plays'].rank(ascending = False)\n",
    "\n",
    "# filtering for their first choice\n",
    "data_1  = data[data['rank'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "336f236b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                                           user     artist  plays  user_id  \\\n",
       "0      00000c289a1829a808ac09c00daf10bc3c4e223b  die Ärzte   1099        0   \n",
       "25686  005d521c9f8b1acfc13b7a4cc4b39085edfc786a  die Ärzte    933      523   \n",
       "29112  006aaaaf386fdbb0aea3f2bf9019e346a7294b6a  die Ärzte    946      595   \n",
       "30440  006f93b4213be13020a3819e0d0a86dcf97b58de  die Ärzte   1375      622   \n",
       "53937  00c465e5b33365ab91cc5cf161590a38044954af  die Ärzte   2924     1094   \n",
       "\n",
       "       artist_id  rank  \n",
       "0          30264   1.0  \n",
       "25686      30264   1.0  \n",
       "29112      30264   1.0  \n",
       "30440      30264   1.0  \n",
       "53937      30264   1.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1[data_1['artist_id'] == 30264].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1e957969",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1094</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>knorkator</td>\n",
       "      <td>920</td>\n",
       "      <td>1094</td>\n",
       "      <td>57157</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
       "      <td>typ:t.u.r.b.o.</td>\n",
       "      <td>911</td>\n",
       "      <td>1094</td>\n",
       "      <td>103029</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53945</th>\n",
       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
       "      <td>serj tankian</td>\n",
       "      <td>813</td>\n",
       "      <td>1094</td>\n",
       "      <td>85997</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53946</th>\n",
       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
       "      <td>rise against</td>\n",
       "      <td>779</td>\n",
       "      <td>1094</td>\n",
       "      <td>82040</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           user            artist  plays  \\\n",
       "53937  00c465e5b33365ab91cc5cf161590a38044954af         die Ärzte   2924   \n",
       "53938  00c465e5b33365ab91cc5cf161590a38044954af       equilibrium   1936   \n",
       "53939  00c465e5b33365ab91cc5cf161590a38044954af         ensiferum   1782   \n",
       "53940  00c465e5b33365ab91cc5cf161590a38044954af  system of a down   1167   \n",
       "53941  00c465e5b33365ab91cc5cf161590a38044954af               sdp   1042   \n",
       "53942  00c465e5b33365ab91cc5cf161590a38044954af         deichkind   1013   \n",
       "53943  00c465e5b33365ab91cc5cf161590a38044954af         knorkator    920   \n",
       "53944  00c465e5b33365ab91cc5cf161590a38044954af    typ:t.u.r.b.o.    911   \n",
       "53945  00c465e5b33365ab91cc5cf161590a38044954af      serj tankian    813   \n",
       "53946  00c465e5b33365ab91cc5cf161590a38044954af      rise against    779   \n",
       "\n",
       "       user_id  artist_id  rank  \n",
       "53937     1094      30264   1.0  \n",
       "53938     1094      36491   2.0  \n",
       "53939     1094      36343   3.0  \n",
       "53940     1094      92524   4.0  \n",
       "53941     1094      85406   5.0  \n",
       "53942     1094      29016   6.0  \n",
       "53943     1094      57157   7.0  \n",
       "53944     1094     103029   8.0  \n",
       "53945     1094      85997   9.0  \n",
       "53946     1094      82040  10.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['user_id'] == 1094].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "78235cbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     artist     score\n",
      "0               tyler bates  1.337788\n",
      "1             jan hegenberg  1.193714\n",
      "2   five finger death punch  1.190098\n",
      "3          slagsmålsklubben  1.178774\n",
      "4              daniel licht  1.163203\n",
      "5         kaizers orchestra  1.141305\n",
      "6                       eav  1.137476\n",
      "7                 fightstar  1.131872\n",
      "8                   flyleaf  1.128522\n",
      "9                   volbeat  1.127203\n",
      "10                   saliva  1.124229\n",
      "11             freedom call  1.123379\n",
      "12         jennifer rostock  1.118862\n",
      "13                  flobots  1.117408\n",
      "14                  skillet  1.115119\n",
      "15       machinae supremacy  1.112002\n",
      "16               eisbrecher  1.111799\n",
      "17                    aiden  1.107183\n",
      "18                      asp  1.102318\n",
      "19            drowning pool  1.102212\n"
     ]
    }
   ],
   "source": [
    "user_id = 1094\n",
    "\n",
    "# Use the implicit recommender.\n",
    "recommended = model.recommend(user_id, sparse_user_item,N = 20,filter_already_liked_items = False)\n",
    "\n",
    "artists = []\n",
    "scores = []\n",
    "\n",
    "# Get artist names from ids\n",
    "for item in recommended:\n",
    "    idx, score = item\n",
    "    artists.append(data.artist.loc[data.artist_id == idx].iloc[0])\n",
    "    scores.append(score)\n",
    "\n",
    "# Create a dataframe of artist names and scores\n",
    "recommendations = pd.DataFrame({'artist': artists, 'score': scores})\n",
    "\n",
    "print (recommendations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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