diff --git a/BPR_Music_RecSys.ipynb b/BPR_Music_RecSys.ipynb
new file mode 100644
index 0000000..8bf356c
--- /dev/null
+++ b/BPR_Music_RecSys.ipynb
@@ -0,0 +1,1239 @@
+{
+ "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": {
+      "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>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": {
+      "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>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": [
+    {
+     "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>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>4</th>\n",
+       "      <td>00000c289a1829a808ac09c00daf10bc3c4e223b</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <td>691</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1422</th>\n",
+       "      <td>000429493d9716b66b02180d208d09b5b89fbe64</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <td>234</td>\n",
+       "      <td>29</td>\n",
+       "      <td>80876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2139</th>\n",
+       "      <td>0007e26aafcfc0b6dcb87d7041583fbb7cced88a</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <td>159</td>\n",
+       "      <td>44</td>\n",
+       "      <td>80876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3284</th>\n",
+       "      <td>000b0bb32f149504e1df3cce85b6bfd20cef3dd0</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <td>46</td>\n",
+       "      <td>68</td>\n",
+       "      <td>80876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3322</th>\n",
+       "      <td>000b2ee840cbda56e0f41c8f248c4fb7ee275db3</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <td>87</td>\n",
+       "      <td>69</td>\n",
+       "      <td>80876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998983</th>\n",
+       "      <td>1d2c0053f7e585caa02ccbb3a96b708c078e9300</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>40892</td>\n",
+       "      <td>80876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999418</th>\n",
+       "      <td>1d2d697dbe47814d1de4064508b32ec00a1ec767</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <td>384</td>\n",
+       "      <td>40901</td>\n",
+       "      <td>80876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999642</th>\n",
+       "      <td>1d2db9f945283c3f48ab685a84a78429a63a3f19</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <td>66</td>\n",
+       "      <td>40905</td>\n",
+       "      <td>80876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999823</th>\n",
+       "      <td>1d2e1fa030318413f194f741c674b6032094a0ce</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <td>389</td>\n",
+       "      <td>40909</td>\n",
+       "      <td>80876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999889</th>\n",
+       "      <td>1d2e583e63cbd9cfc526b53f90f4fd438271336e</td>\n",
+       "      <td>red hot chili peppers</td>\n",
+       "      <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": {
+      "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>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>2943</th>\n",
+       "      <td>000a1585c5f65532a9c9187a882892982d345a5c</td>\n",
+       "      <td>die Ärzte</td>\n",
+       "      <td>148</td>\n",
+       "      <td>61</td>\n",
+       "      <td>30264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3787</th>\n",
+       "      <td>000cb6427411006fe9a6193d3c4f59efed53fbef</td>\n",
+       "      <td>die Ärzte</td>\n",
+       "      <td>7</td>\n",
+       "      <td>78</td>\n",
+       "      <td>30264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6295</th>\n",
+       "      <td>0014ffc91d3a5b59cce9bceaf22ef0d72e5711b8</td>\n",
+       "      <td>die Ärzte</td>\n",
+       "      <td>88</td>\n",
+       "      <td>128</td>\n",
+       "      <td>30264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13513</th>\n",
+       "      <td>003059a886782e4d7936da913d3f064f637d0b2b</td>\n",
+       "      <td>die Ärzte</td>\n",
+       "      <td>5</td>\n",
+       "      <td>274</td>\n",
+       "      <td>30264</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": {
+      "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>artist</th>\n",
+       "      <th>plays</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>artist_id</th>\n",
+       "      <th>rank</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",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25686</th>\n",
+       "      <td>005d521c9f8b1acfc13b7a4cc4b39085edfc786a</td>\n",
+       "      <td>die Ärzte</td>\n",
+       "      <td>933</td>\n",
+       "      <td>523</td>\n",
+       "      <td>30264</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29112</th>\n",
+       "      <td>006aaaaf386fdbb0aea3f2bf9019e346a7294b6a</td>\n",
+       "      <td>die Ärzte</td>\n",
+       "      <td>946</td>\n",
+       "      <td>595</td>\n",
+       "      <td>30264</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30440</th>\n",
+       "      <td>006f93b4213be13020a3819e0d0a86dcf97b58de</td>\n",
+       "      <td>die Ärzte</td>\n",
+       "      <td>1375</td>\n",
+       "      <td>622</td>\n",
+       "      <td>30264</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53937</th>\n",
+       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
+       "      <td>die Ärzte</td>\n",
+       "      <td>2924</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>30264</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": {
+      "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>artist</th>\n",
+       "      <th>plays</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>artist_id</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>53937</th>\n",
+       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
+       "      <td>die Ärzte</td>\n",
+       "      <td>2924</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>30264</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53938</th>\n",
+       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
+       "      <td>equilibrium</td>\n",
+       "      <td>1936</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>36491</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53939</th>\n",
+       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
+       "      <td>ensiferum</td>\n",
+       "      <td>1782</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>36343</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53940</th>\n",
+       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
+       "      <td>system of a down</td>\n",
+       "      <td>1167</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>92524</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53941</th>\n",
+       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
+       "      <td>sdp</td>\n",
+       "      <td>1042</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>85406</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53942</th>\n",
+       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
+       "      <td>deichkind</td>\n",
+       "      <td>1013</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>29016</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53943</th>\n",
+       "      <td>00c465e5b33365ab91cc5cf161590a38044954af</td>\n",
+       "      <td>knorkator</td>\n",
+       "      <td>920</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>57157</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53944</th>\n",
+       "      <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,
+   "id": "8bd6eee9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}