RecSys_all_predict_ratings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/yjeong5126/movie_recommender/blob/master/item_based_collaborative_filtering/item_based_collaborative_filtering.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Approaches for Recommender Systems\n",
    "## 1. User-based Collaborative Filtering\n",
    "## 2. Item-based Collaborative Filtering\n",
    "## 3. Latent Factor Approach: Matrix Factorization (using SVC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/solarspaceclouds/Desktop/RecommendationSystem/recsys/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from surprise import Dataset, Reader, SVD\n",
    "from surprise.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1031175"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings = pd.read_csv(\"all_ratings.csv\", index_col=False)\n",
    "len(all_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_ratings = all_ratings[:30000] # subset chosen to address memory constraints in later parts of pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_user_ids = all_ratings.userId.unique()\n",
    "# reindex the userId\n",
    "user_id_mapping = {user_id: i + 1 for i, user_id in enumerate(unique_user_ids)}\n",
    "\n",
    "# Map the 'userId' column using the mapping dictionary\n",
    "all_ratings['userId'] = all_ratings['userId'].map(user_id_mapping)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'ISBN', 'bookRating', 'bookTitle', 'bookAuthor',\n",
       "       'yearOfPublication', 'publisher', 'location', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all_ratings = all_ratings.drop(['Unnamed: 0'],axis=1)\n",
    "# all_ratings = all_ratings.reset_index(drop=True)\n",
    "all_ratings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>bookRating</th>\n",
       "      <th>bookTitle</th>\n",
       "      <th>bookAuthor</th>\n",
       "      <th>yearOfPublication</th>\n",
       "      <th>publisher</th>\n",
       "      <th>location</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>034545104X</td>\n",
       "      <td>0</td>\n",
       "      <td>Flesh Tones: A Novel</td>\n",
       "      <td>M. J. Rose</td>\n",
       "      <td>2002</td>\n",
       "      <td>Ballantine Books</td>\n",
       "      <td>tyler, texas, usa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>034545104X</td>\n",
       "      <td>5</td>\n",
       "      <td>Flesh Tones: A Novel</td>\n",
       "      <td>M. J. Rose</td>\n",
       "      <td>2002</td>\n",
       "      <td>Ballantine Books</td>\n",
       "      <td>cincinnati, ohio, usa</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0812533550</td>\n",
       "      <td>9</td>\n",
       "      <td>Ender's Game (Ender Wiggins Saga (Paperback))</td>\n",
       "      <td>Orson Scott Card</td>\n",
       "      <td>1986</td>\n",
       "      <td>Tor Books</td>\n",
       "      <td>cincinnati, ohio, usa</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0679745580</td>\n",
       "      <td>8</td>\n",
       "      <td>In Cold Blood (Vintage International)</td>\n",
       "      <td>TRUMAN CAPOTE</td>\n",
       "      <td>1994</td>\n",
       "      <td>Vintage</td>\n",
       "      <td>cincinnati, ohio, usa</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0060173289</td>\n",
       "      <td>9</td>\n",
       "      <td>Divine Secrets of the Ya-Ya Sisterhood : A Novel</td>\n",
       "      <td>Rebecca Wells</td>\n",
       "      <td>1996</td>\n",
       "      <td>HarperCollins</td>\n",
       "      <td>cincinnati, ohio, usa</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId        ISBN  bookRating  \\\n",
       "0       1  034545104X           0   \n",
       "1       2  034545104X           5   \n",
       "2       2  0812533550           9   \n",
       "3       2  0679745580           8   \n",
       "4       2  0060173289           9   \n",
       "\n",
       "                                          bookTitle        bookAuthor  \\\n",
       "0                              Flesh Tones: A Novel        M. J. Rose   \n",
       "1                              Flesh Tones: A Novel        M. J. Rose   \n",
       "2     Ender's Game (Ender Wiggins Saga (Paperback))  Orson Scott Card   \n",
       "3             In Cold Blood (Vintage International)     TRUMAN CAPOTE   \n",
       "4  Divine Secrets of the Ya-Ya Sisterhood : A Novel     Rebecca Wells   \n",
       "\n",
       "   yearOfPublication         publisher               location   age  \n",
       "0               2002  Ballantine Books      tyler, texas, usa   NaN  \n",
       "1               2002  Ballantine Books  cincinnati, ohio, usa  23.0  \n",
       "2               1986         Tor Books  cincinnati, ohio, usa  23.0  \n",
       "3               1994           Vintage  cincinnati, ohio, usa  23.0  \n",
       "4               1996     HarperCollins  cincinnati, ohio, usa  23.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_to_check = \"The Great Gatsby\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Users who have read 'The Great Gatsby':\n",
      "[29, 70]\n"
     ]
    }
   ],
   "source": [
    "def users_read_book(book_title):\n",
    "    users_list = all_ratings[all_ratings['bookTitle'] == book_title]['userId'].tolist()\n",
    "    return users_list\n",
    "\n",
    "users_list_for_book = users_read_book(item_to_check)\n",
    "\n",
    "print(f\"Users who have read '{item_to_check}':\")\n",
    "print(users_list_for_book)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "       52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n",
       "       69, 70])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings.userId.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_ratings = all_ratings[['bookTitle','userId','bookRating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22481"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings.bookTitle.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from surprise import Dataset\n",
    "from surprise import Reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "       52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n",
       "       69, 70])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings.userId.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = Reader(rating_scale=(1, 10))\n",
    "data = Dataset.load_from_df(all_ratings[[\"bookTitle\",\"userId\",  \"bookRating\"]], reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import KNNWithMeans\n",
    "\n",
    "# To use item-based cosine similarity\n",
    "sim_options = {\n",
    "    \"name\": \"cosine\",\n",
    "    \"user_based\": False,  # Compute  similarities between items\n",
    "}\n",
    "algo = KNNWithMeans(sim_options=sim_options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_ratings = all_ratings[all_ratings['bookRating']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  9,  8,  7,  6, 10,  3,  4,  2,  1])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings.bookRating.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Flesh Tones: A Novel',\n",
       "       \"Ender's Game (Ender Wiggins Saga (Paperback))\",\n",
       "       'In Cold Blood (Vintage International)', ..., 'Heartburn',\n",
       "       'Martina', 'The First Salute'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings.bookTitle.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "desired_bookTitle = 'Seal it with a Kiss'\n",
    "# desired_bookTitle = 'The Great Gatsby'\n",
    "specified_userid = 2\n",
    "already_read_list = list(all_ratings[all_ratings['userId'] == specified_userid]['bookTitle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = all_ratings[['bookTitle','userId','bookRating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bookTitle</th>\n",
       "      <th>userId</th>\n",
       "      <th>bookRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Flesh Tones: A Novel</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ender's Game (Ender Wiggins Saga (Paperback))</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>In Cold Blood (Vintage International)</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Divine Secrets of the Ya-Ya Sisterhood : A Novel</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Mistress of Spices</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          bookTitle  userId  bookRating\n",
       "1                              Flesh Tones: A Novel       2           5\n",
       "2     Ender's Game (Ender Wiggins Saga (Paperback))       2           9\n",
       "3             In Cold Blood (Vintage International)       2           8\n",
       "4  Divine Secrets of the Ya-Ya Sisterhood : A Novel       2           9\n",
       "5                            The Mistress of Spices       2           5"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.knns.KNNWithMeans at 0x7f796c4bfbb0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = KNNWithMeans(sim_options=sim_options)\n",
    "\n",
    "# all_ratings = all_ratings[all_ratings['bookRating']!=0]\n",
    "reader = Reader(rating_scale=(1, 10))\n",
    "\n",
    "data = Dataset.load_from_df(all_ratings[[\"userId\", \"bookTitle\", \"bookRating\"]], reader)\n",
    "trainingSet = data.build_full_trainset()\n",
    "\n",
    "algo.fit(trainingSet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of a predicted book rating for a book which a specified user has not read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Book Rating Prediction is 6.0 for Seal it with a Kiss for user:2\n"
     ]
    }
   ],
   "source": [
    "# data = Dataset.load_from_df(all_ratings[[\"bookTitle\",\"userId\",  \"bookRating\"]], reader)\n",
    "prediction = algo.predict(specified_userid, desired_bookTitle)\n",
    "prediction.est\n",
    "\n",
    "print(f\"Book Rating Prediction is {prediction.est} for {desired_bookTitle} for user:{specified_userid}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended books for user 2 :\n",
      "['Roses Are Red (Alex Cross Novels)',\n",
      " 'Toot & Puddle',\n",
      " 'A Little Look-See:  Mutts 6',\n",
      " 'The Highwayman',\n",
      " 'The Essential Calvin and Hobbes',\n",
      " \"Lamb : The Gospel According to Biff, Christ's Childhood Pal\",\n",
      " 'Howl and Other Poems (Pocket Poets)',\n",
      " 'A Dog Year: Twelve Months, Four Dogs, and Me',\n",
      " 'Basket Case',\n",
      " 'Shinju',\n",
      " 'E-Wally and the Quest',\n",
      " 'Border Music',\n",
      " 'Twas the Night Before: A Love Story',\n",
      " 'Quentins',\n",
      " 'Standing in the Rainbow : A Novel',\n",
      " 'Five Quarters of the Orange',\n",
      " '1984',\n",
      " 'Flesh Tones: A Novel',\n",
      " 'Wuthering Heights',\n",
      " 'She Shall Have Murder (Perennial Library, P638)',\n",
      " 'Flesh Tones: A Novel',\n",
      " 'To Kill a Mockingbird',\n",
      " 'Little Altars Everywhere: A Novel',\n",
      " 'The Red Tent (Bestselling Backlist)',\n",
      " 'Good in Bed',\n",
      " 'Lucky Man: A Memoir',\n",
      " \"The Liar's Club: A Memoir\",\n",
      " 'Brothel: Mustang Ranch and Its Women',\n",
      " 'Rosie: A Novel',\n",
      " 'The Territory of Men',\n",
      " 'Blues Dancing: A Novel',\n",
      " 'Every Womans Health the Complete Guide to Body',\n",
      " 'DK Handbooks: Dogs',\n",
      " 'Blues: For All the Changes : New Poems',\n",
      " 'The Autobiography of Malcolm X',\n",
      " 'The Good Housekeeping Household Encyclopedia',\n",
      " 'An Essential Guide to Choosing Your Tropical Freshwater Fish (Tankmasters '\n",
      " 'Series)',\n",
      " 'The Portable Pediatrician for Parents',\n",
      " 'Ghosts of Mississippi: The Murder of Medgar Evers, the Trials of Byron De LA '\n",
      " 'Beckwith, and the Haunting of the New South (Ghosts of Mississippi)',\n",
      " \"Stanley Newman's Coffee Time Word Games\",\n",
      " 'Flesh Tones: A Novel',\n",
      " \"The View from Nowhere: The Only Bar Guide You'll Ever Want -- Or Need\",\n",
      " 'The Hunt for Red October',\n",
      " 'The English Patient',\n",
      " 'Uh-Oh: Some Observations from Both Sides of the Refrigerator Door',\n",
      " 'Kids Online: Protecting Your Children in Cyberspace',\n",
      " 'Lake Wobegon Days',\n",
      " 'Mariette in Ecstasy',\n",
      " 'Farewell, My Lovely (Vintage CrimeBlack Lizard)',\n",
      " 'Cries From the Heart',\n",
      " 'Icebound',\n",
      " \"Boy's Life\",\n",
      " \"Left Behind: A Novel of the Earth's Last Days (Left Behind No. 1)\",\n",
      " 'Without Due Process',\n",
      " 'The Laws of Our Fathers',\n",
      " 'Womenfolks: Growing Up Down South',\n",
      " 'Flesh Tones: A Novel',\n",
      " 'Wild Animus',\n",
      " \"Linda McCartney's Sixties : Portrait of an Era\",\n",
      " 'Everville : The Second Book of the Art',\n",
      " 'Jitterbug Perfume',\n",
      " 'The Language of Threads: A Novel',\n",
      " 'The Memoirs of Bambi Goldbloom, Or, Growing Up in New Jersey',\n",
      " 'The Ground Beneath Her Feet',\n",
      " 'The RIVER MIDNIGHT : A NOVEL',\n",
      " 'Crazy Ladies : A Novel',\n",
      " 'Starfollowers of Coramonde',\n",
      " 'Koko']\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "# Get the user's top N recommendations\n",
    "k = 10  \n",
    "num_recs = 10\n",
    "user_inner_id = algo.trainset.to_inner_uid(specified_userid)\n",
    "user_neighbors = algo.get_neighbors(user_inner_id, k=k)\n",
    "\n",
    "# Convert inner IDs back to the original user IDs\n",
    "user_neighbors = [algo.trainset.to_raw_uid(inner_id) for inner_id in user_neighbors]\n",
    "\n",
    "# Get list of user's nearest neighbors based on the trained model\n",
    "# --> Recommend books from these nearest neighbors' rated books\n",
    "recommended_books = []\n",
    "\n",
    "for neighbor_userid in user_neighbors:\n",
    "    neighbor_ratings = all_ratings[all_ratings[\"userId\"] == neighbor_userid]\n",
    "\n",
    "    # Filter out books that the specified user has already rated\n",
    "    # already_read_list = list(all_ratings[all_ratings['userId'] == specified_userid]['bookTitle'])\n",
    "    neighbor_ratings = neighbor_ratings[~neighbor_ratings[\"bookTitle\"].isin([already_read_list])] \n",
    "    \n",
    "\n",
    "    # Sort by bookRating in descending order to recommend the highest-rated books\n",
    "    neighbor_ratings = neighbor_ratings.sort_values(by=\"bookRating\", ascending=False)\n",
    "\n",
    "    # take the top-rated books from each neighbor and add them to the recommended_books list\n",
    "    top_n_books = neighbor_ratings.head(num_recs)  # You can change the number of recommendations as needed\n",
    "    recommended_books.extend(top_n_books[\"bookTitle\"].tolist())\n",
    "\n",
    "# 'recommended_books' now contains the recommended books for the specified user based on their nearest neighbors' preferences\n",
    "print(\"Recommended books for user\", specified_userid, \":\")\n",
    "pprint(recommended_books)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User-based Collaborative Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "1.676052246732306\n",
      "{'sim_options': {'name': 'cosine', 'min_support': 4, 'user_based': True}}\n"
     ]
    }
   ],
   "source": [
    "from surprise import KNNWithMeans\n",
    "from surprise import Dataset\n",
    "from surprise.model_selection import GridSearchCV\n",
    "\n",
    "# Gridsearch to get the best User-based Collaborative filtering model\n",
    "sim_options = {\n",
    "    \"name\": [\"msd\", \"cosine\"],\n",
    "    \"min_support\": [3, 4, 5],\n",
    "    \"user_based\": [True],\n",
    "}\n",
    "\n",
    "param_grid = {\"sim_options\": sim_options}\n",
    "\n",
    "gs = GridSearchCV(KNNWithMeans, param_grid, measures=[\"rmse\", \"mae\"], cv=3)\n",
    "gs.fit(data)\n",
    "\n",
    "print(gs.best_score[\"rmse\"])\n",
    "print(gs.best_params[\"rmse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.knns.KNNWithMeans at 0x7f796c4be6e0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_options = gs.best_params[\"rmse\"]['sim_options']\n",
    "algo = KNNWithMeans(sim_options=sim_options)\n",
    "\n",
    "all_ratings = all_ratings[all_ratings['bookRating']!=0]\n",
    "reader = Reader(rating_scale=(1, 10))\n",
    "reader = Dataset.load_from_df(all_ratings[['userId', 'bookTitle', 'bookRating']], reader).build_full_trainset()\n",
    "algo.fit(reader)\n",
    "\n",
    "data = Dataset.load_from_df(all_ratings[[\"userId\", \"bookTitle\", \"bookRating\"]], reader)\n",
    "trainingSet = data.build_full_trainset()\n",
    "\n",
    "algo.fit(trainingSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the user's top N recommendations\n",
    "k = 5  # k num of neighbours\n",
    "num_recs = 10 # N num of recommendations\n",
    "\n",
    "user_inner_id = algo.trainset.to_inner_uid(specified_userid)\n",
    "user_neighbors = algo.get_neighbors(user_inner_id, k=k)\n",
    "\n",
    "# Convert inner IDs back to the original user IDs\n",
    "user_neighbors = [algo.trainset.to_raw_uid(inner_id) for inner_id in user_neighbors]\n",
    "\n",
    "#Get list of user's nearest neighbors based on the trained model\n",
    "# -->Recommend books from these nearest neighbors' rated books\n",
    "recommended_books = []\n",
    "\n",
    "for neighbor_userid in user_neighbors:\n",
    "    \n",
    "    neighbor_ratings = all_ratings[all_ratings[\"userId\"] == neighbor_userid]\n",
    "\n",
    "    # Filter out books that the specified user has already rated\n",
    "    # already_read_list = list(all_ratings[all_ratings['userId'] == specified_userid]['bookTitle'])\n",
    "    neighbor_ratings = neighbor_ratings[~neighbor_ratings[\"bookTitle\"].isin([already_read_list])] \n",
    "    \n",
    "\n",
    "    # Sort by bookRating in descending order to recommend the highest-rated books\n",
    "    neighbor_ratings = neighbor_ratings.sort_values(by=\"bookRating\", ascending=False)\n",
    "\n",
    "    # take the top-rated books from each neighbor and add them to the recommended_books list\n",
    "    top_n_books = neighbor_ratings.head(num_recs)  \n",
    "    recommended_books.extend(top_n_books[\"bookTitle\"].tolist())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(recommended_books)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended books for user 2 :\n",
      "['Go Ask Alice (AvonFlare Book)',\n",
      " 'The First Salute',\n",
      " 'The Notebook',\n",
      " 'A Painted House',\n",
      " 'Mythology',\n",
      " 'Flatland (Shambhala Pocket Classics)',\n",
      " 'Waiting For Nick (Silhouette Special Edition)',\n",
      " \"Girls' Poker Night\",\n",
      " 'Up & Out (Red Dress Ink)',\n",
      " 'Geek Love',\n",
      " 'Roses Are Red (Alex Cross Novels)',\n",
      " 'Toot & Puddle',\n",
      " 'A Little Look-See:  Mutts 6',\n",
      " 'The Highwayman',\n",
      " 'The Essential Calvin and Hobbes',\n",
      " \"Lamb : The Gospel According to Biff, Christ's Childhood Pal\",\n",
      " 'Howl and Other Poems (Pocket Poets)',\n",
      " 'A Dog Year: Twelve Months, Four Dogs, and Me',\n",
      " 'Basket Case',\n",
      " 'Shinju',\n",
      " 'E-Wally and the Quest',\n",
      " 'Border Music',\n",
      " 'Twas the Night Before: A Love Story',\n",
      " 'Quentins',\n",
      " 'Standing in the Rainbow : A Novel',\n",
      " 'Five Quarters of the Orange',\n",
      " '1984',\n",
      " 'Flesh Tones: A Novel',\n",
      " 'Wuthering Heights',\n",
      " 'She Shall Have Murder (Perennial Library, P638)',\n",
      " 'Flesh Tones: A Novel',\n",
      " 'To Kill a Mockingbird',\n",
      " 'Little Altars Everywhere: A Novel',\n",
      " 'The Red Tent (Bestselling Backlist)',\n",
      " 'Good in Bed',\n",
      " 'Lucky Man: A Memoir',\n",
      " \"The Liar's Club: A Memoir\",\n",
      " 'Brothel: Mustang Ranch and Its Women',\n",
      " 'Rosie: A Novel',\n",
      " 'The Territory of Men',\n",
      " 'Blues Dancing: A Novel',\n",
      " 'Every Womans Health the Complete Guide to Body',\n",
      " 'DK Handbooks: Dogs',\n",
      " 'Blues: For All the Changes : New Poems',\n",
      " 'The Autobiography of Malcolm X',\n",
      " 'The Good Housekeeping Household Encyclopedia',\n",
      " 'An Essential Guide to Choosing Your Tropical Freshwater Fish (Tankmasters '\n",
      " 'Series)',\n",
      " 'The Portable Pediatrician for Parents',\n",
      " 'Ghosts of Mississippi: The Murder of Medgar Evers, the Trials of Byron De LA '\n",
      " 'Beckwith, and the Haunting of the New South (Ghosts of Mississippi)',\n",
      " \"Stanley Newman's Coffee Time Word Games\"]\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "print(\"Recommended books for user\", specified_userid, \":\")\n",
    "pprint(recommended_books)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Item-based Collaborative Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "1.6824933355409228\n",
      "{'sim_options': {'name': 'cosine', 'min_support': 5, 'user_based': False}}\n"
     ]
    }
   ],
   "source": [
    "from surprise import KNNWithMeans\n",
    "from surprise import Dataset\n",
    "from surprise.model_selection import GridSearchCV\n",
    "\n",
    "data = Dataset.load_from_df(df, reader)\n",
    "sim_options = {\n",
    "    \"name\": [\"msd\", \"cosine\"],\n",
    "    \"min_support\": [3, 4, 5],\n",
    "    \"user_based\": [False],\n",
    "}\n",
    "\n",
    "param_grid = {\"sim_options\": sim_options}\n",
    "\n",
    "gs = GridSearchCV(KNNWithMeans, param_grid, measures=[\"rmse\", \"mae\"], cv=3)\n",
    "gs.fit(data)\n",
    "\n",
    "print(gs.best_score[\"rmse\"])\n",
    "print(gs.best_params[\"rmse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.knns.KNNWithMeans at 0x7f796c4bf340>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_options = gs.best_params[\"rmse\"]['sim_options']\n",
    "algo = KNNWithMeans(sim_options=sim_options)\n",
    "\n",
    "all_ratings = all_ratings[all_ratings['bookRating']!=0]\n",
    "reader = Reader(rating_scale=(1, 10))\n",
    "reader = Dataset.load_from_df(all_ratings[['userId', 'bookTitle', 'bookRating']], reader).build_full_trainset()\n",
    "algo.fit(reader)\n",
    "\n",
    "data = Dataset.load_from_df(all_ratings[[\"userId\", \"bookTitle\", \"bookRating\"]], reader)\n",
    "trainingSet = data.build_full_trainset()\n",
    "\n",
    "algo.fit(trainingSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(\"Murder in Scorpio (St. Martin's Dead Letter Mysteries,)\",\n",
       "  8.233807789953273),\n",
       " ('In the Spirit', 8.233807582495153),\n",
       " ('The Hundredth Man', 8.233807233561642),\n",
       " ('Love for Sale : A Grace & Favor Mystery (Grace & Favor Mysteries (Hardcover))',\n",
       "  8.233807152714514),\n",
       " ('The Vampire Armand: The Vampire Chronicles (Rice, Anne, Vampire Chronicles.)',\n",
       "  8.233806616376832),\n",
       " (\"Ahab's Wife: Or, The Star-Gazer: A Novel\", 8.233804114523924),\n",
       " ('Transcension', 8.233802919435),\n",
       " ('Sadako and the Thousand Paper Cranes', 8.233801693749507),\n",
       " ('Second Touch (A. D. Chronicles #2)', 8.233801586081743),\n",
       " ('In the Company of Soldiers : A Chronicle of Combat', 8.233801559082298),\n",
       " ('Is that a Moose in Your Pocket?', 8.233800673721543),\n",
       " ('Playing For Keeps', 8.233800589878834),\n",
       " ('The Sea Hunters II', 8.233800220561681),\n",
       " ('Le Grand Meaulnes (Classiques De Poche)', 8.233800040567276),\n",
       " ('Dangerous Dilemmas', 8.233799525026312),\n",
       " ('McVicar by Himself', 8.23379941477722),\n",
       " ('Darknesses (The Corean Chronicles, Book 2)', 8.233799371538204),\n",
       " (\"The Warrior's Damsel\", 8.233799057552522),\n",
       " ('The Puzzle Bark Tree', 8.233796513804693),\n",
       " ('The Winter Mantle', 8.233795694962277),\n",
       " ('Black Rosebud: Have No Mercy II', 8.233795137916298),\n",
       " ('Critical Space', 8.233795086515247),\n",
       " ('Unleashed : Of Poltergeists and Murder: The Curious Story of Tina Resch',\n",
       "  8.233795005545977),\n",
       " ('Island', 8.233793688950716),\n",
       " ('Exit Strategies: A Novel', 8.233792945242868),\n",
       " ('Speechless (Red Dress Ink)', 8.233792094338874),\n",
       " ('Political Suicide : A Novel (Na)', 8.233792000582584),\n",
       " ('The Wife of Reilly', 8.233791541976831),\n",
       " ('Moon Racer', 8.233791057402247),\n",
       " ('Blind Sight (Moving Fiction)', 8.23379076648973),\n",
       " ('Cape Light', 8.233789480699826),\n",
       " ('Because of Winn-Dixie', 8.233788124973744),\n",
       " ('Forever and Always', 8.233788079190049),\n",
       " ('DEALING WITH THE IDIOTS IN YOUR LIFE', 8.233787959784019),\n",
       " ('Hugs for Women on the Go: Stories, Sayings, and Scriptures to Encourage and Inspire',\n",
       "  8.233787755135898),\n",
       " ('Misery', 8.233785599221202),\n",
       " ('My Sergei: A Love Story', 8.233785401493819),\n",
       " ('Edgewater', 8.233785017098354),\n",
       " ('Opposites (First Concepts)', 8.233784893815297),\n",
       " ('Dean Koontz: 3 Complete Novels (Dark Rivers of the Heart  Sole Survivor  Intensity)',\n",
       "  8.233784812390553),\n",
       " (\"The Devil's Apprentice: A Novel\", 8.233782729444018),\n",
       " ('Bedtime Stories For Cats', 8.233782393682056),\n",
       " ('More Shtuff - Mutts Iii (Mutts)', 8.233776180852209),\n",
       " ('Warchild', 8.233776028696575),\n",
       " ('Babylon Rising', 8.233774761351398),\n",
       " (\"Breakfast at Bethany's (Harlequin Temptation)\", 8.233774392408975),\n",
       " ('Firethorn', 8.23377421650399),\n",
       " ('Cat in the Hat (I Can Read It All by Myself Beginner Books)',\n",
       "  8.233772932648384),\n",
       " ('Cat calls: Cat-egories for the naming of cats', 8.233772780584664),\n",
       " ('Moon in the Water', 8.233771519034471),\n",
       " (\"Freedom's Landing\", 8.233770938424016),\n",
       " ('Un sac de billes', 8.233769864394882),\n",
       " ('Earth Song', 8.233767674206913),\n",
       " ('Mine Eyes Have Seen', 8.233767423339074),\n",
       " ('Best Remembered Poems', 8.233767227664876),\n",
       " ('The Ways of Grace', 8.233766843726823),\n",
       " ('Eva Moves the Furniture', 8.233766650085121),\n",
       " (\"The Snake Catcher's Daughter:  A Mamur Zapt Mystery\", 8.233765836684148),\n",
       " ('Wedding the Highlander (Highlander Trilogy)', 8.233764798648977),\n",
       " ('Red Hook', 8.233764669921552),\n",
       " ('Extremes: A Retrieval Artist Novel (Retrieval Artist Novels)',\n",
       "  8.233763040599468),\n",
       " ('Candy', 8.233762882339388),\n",
       " ('Neanderthal', 8.233762583625635),\n",
       " ('Missing Pieces', 8.233761494150167),\n",
       " ('One Step Behind (Vintage CrimeBlack Lizard)', 8.233761035920706),\n",
       " ('Temple', 8.233759735113335),\n",
       " ('L Is for Lawless', 8.233759723134694),\n",
       " ('Clinging to the Wreckage', 8.233759694193555),\n",
       " ('Second Contact', 8.23375935688976),\n",
       " (\"The Quilter's Apprentice\", 8.233758824719265),\n",
       " ('A Dog Year: Twelve Months, Four Dogs, and Me', 8.233758003364462),\n",
       " ('His Child Or Hers? (Count On A Cop) (Harlequin Superromance, No 993)',\n",
       "  8.233757641558137),\n",
       " ('Poison in the Pen', 8.233756982755388),\n",
       " ('Billionaire Is Back (Silhouette Romance, No 1520)', 8.233756197609793),\n",
       " ('Fault Lines', 8.233755825783824),\n",
       " ('Deadly Associations', 8.233754807376576),\n",
       " ('Widow of Jerusalem: A Medieval Mystery', 8.233754108258687),\n",
       " ('In the Hand of Dante: A Novel', 8.233753356522243),\n",
       " ('Best Friends', 8.233753330134073),\n",
       " ('Dead Guilty', 8.233753182367048),\n",
       " ('The Horse Whisperer', 8.233753094208636),\n",
       " ('The Very Daring Duchess (Sonnet Books)', 8.233752654441457),\n",
       " ('Cat on the Edge : A Joe Grey Mystery', 8.233752098439778),\n",
       " ('Tales of Mystery and Terror (Puffin Classics)', 8.23375127295282),\n",
       " ('The Waste Lands (The Dark Tower, Book 3)', 8.2337510307614),\n",
       " ('Country Woman Christmas 1998', 8.23374940393252),\n",
       " (\"St. Patrick's Bed (Ashland)\", 8.233748435640981),\n",
       " (\"At Wick's End (Prime Crime Mysteries)\", 8.233747748774874),\n",
       " ('The Lost Ball, La pelota perdida', 8.233746683343504),\n",
       " ('Go Ask Alice (AvonFlare Book)', 8.233746418472116),\n",
       " (\"Suzanne's Diary for Nicholas\", 8.233746351813423),\n",
       " (\"The Viscount's Bawdy Bargain\", 8.233746331166024),\n",
       " ('Blood, Money & Power: How L.B.J. Killed J.F.K.', 8.233746297176683),\n",
       " ('Radio Activity (Fitzhugh, Bill)', 8.233745819056832),\n",
       " ('Neanderthal: A Novel', 8.233745803015863),\n",
       " ('Knife Self-Defense for Combat (Special ForcesRanger-UdtSeal Hand-To-Hand CombatSpecial W)',\n",
       "  8.23374563891687),\n",
       " ('Hidden Jewel (Landry)', 8.233745428996784),\n",
       " ('Curanderismo: Mexican American Folk Healing', 8.233744025132841),\n",
       " ('Splendid Omens', 8.233743778391776),\n",
       " ('Shadow of the Storm', 8.233743743557028),\n",
       " ('Three Poems', 8.233743517965623),\n",
       " ('Short Story Masterpieces', 8.233743198110625),\n",
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       " ('A Brief History of Time : The Updated and Expanded Tenth Anniversary Edition',\n",
       "  8.233008645187786),\n",
       " ('The Destroyer Goddess (In Fire Forged, Part 2)', 8.23300703866192),\n",
       " (\"Life's Little Instruction Book (Life's Little Instruction Books (Paperback))\",\n",
       "  8.23300674274164),\n",
       " ...]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "# Get the user's top N recommendations\n",
    "k = 10  \n",
    "num_recs = 10 # = N\n",
    "\n",
    "\n",
    "# Get the inner user ID for the specified user\n",
    "user_inner_id = algo.trainset.to_inner_uid(specified_userid)\n",
    "\n",
    "# Get the raw item IDs and their predicted ratings for the specified user\n",
    "user_items_ratings = []\n",
    "for item_inner_id in algo.trainset.all_items():\n",
    "    if not algo.trainset.knows_user(user_inner_id) or algo.trainset.to_raw_iid(item_inner_id) in algo.trainset.ur[user_inner_id]:\n",
    "        continue\n",
    "    predicted_rating = algo.predict(user_inner_id, item_inner_id).est\n",
    "\n",
    "    # Add a random perturbation factor to the predicted rating\n",
    "    random_factor = random.uniform(0.001, 0.01)  # You can adjust this range as needed\n",
    "    predicted_rating += random_factor\n",
    "\n",
    "    item_raw_id = algo.trainset.to_raw_iid(item_inner_id)\n",
    "    user_items_ratings.append((item_raw_id, predicted_rating))\n",
    "\n",
    "# Sort the items by predicted ratings in descending order\n",
    "user_items_ratings.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "user_items_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended books for user 2 :\n",
      "Murder in Scorpio (St. Martin's Dead Letter Mysteries,)\n",
      "In the Spirit\n",
      "The Hundredth Man\n",
      "Love for Sale : A Grace & Favor Mystery (Grace & Favor Mysteries (Hardcover))\n",
      "The Vampire Armand: The Vampire Chronicles (Rice, Anne, Vampire Chronicles.)\n",
      "Ahab's Wife: Or, The Star-Gazer: A Novel\n",
      "Transcension\n",
      "Sadako and the Thousand Paper Cranes\n",
      "Second Touch (A. D. Chronicles #2)\n",
      "In the Company of Soldiers : A Chronicle of Combat\n"
     ]
    }
   ],
   "source": [
    "# Extract the top-N recommended item raw IDs (book titles)\n",
    "top_n_items = user_items_ratings[:num_recs]\n",
    "\n",
    "# 'top_n_items' contains tuples of (book title, predicted rating)\n",
    "# Extract the book titles from the tuples and print the recommended book titles\n",
    "recommended_books = [item[0] for item in top_n_items]\n",
    "\n",
    "print(\"Recommended books for user\", specified_userid, \":\")\n",
    "for book_title in recommended_books:\n",
    "    print(book_title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latent Factor Approach: Matrix Factorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5041296228170777\n",
      "{'n_factors': 7, 'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4}\n"
     ]
    }
   ],
   "source": [
    "# check best values for SVD algo (matrix factorization algo)\n",
    "from surprise import SVD\n",
    "from surprise import Dataset\n",
    "from surprise.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    \"n_factors\":[i for i in range(30)],\n",
    "    \"n_epochs\": [5, 10],\n",
    "    \"lr_all\": [0.002, 0.005],\n",
    "    \"reg_all\": [0.4, 0.6]\n",
    "}\n",
    "gs = GridSearchCV(SVD, param_grid, measures=[\"rmse\", \"mae\"], cv=3)\n",
    "\n",
    "gs.fit(data)\n",
    "\n",
    "print(gs.best_score[\"rmse\"])\n",
    "print(gs.best_params[\"rmse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.knns.KNNWithMeans at 0x7f796c4beb30>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from surprise import KNNWithMeans\n",
    "\n",
    "sim_options = gs.best_params[\"rmse\"]\n",
    "algo = KNNWithMeans(sim_options=sim_options)\n",
    "\n",
    "# all_ratings = all_ratings[all_ratings['bookRating']!=0]\n",
    "reader = Reader(rating_scale=(1, 10))\n",
    "\n",
    "data = Dataset.load_from_df(all_ratings[[\"userId\", \"bookTitle\", \"bookRating\"]], reader)\n",
    "trainingSet = data.build_full_trainset()\n",
    "\n",
    "algo.fit(trainingSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended books for user 2 :\n",
      "['The Poisonwood Bible: A Novel', 'Deception on His Mind', \"Complications: A Surgeon's Notes on an Imperfect Science\", 'The Other Boleyn Girl', 'Mortal Sins', 'The Poyson Garden: An Elizabethan I Mystery (Elizabeth I Mysteries (Paperback))', \"Help I'm a Parent\", 'Dark Nantucket Noon: A Homer Kelly Mystery', \"The Quilter's Apprentice\", 'The English Assassin']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Get the user's top N recommendations\n",
    "k = 10  \n",
    "num_recs = 10\n",
    "user_inner_id = algo.trainset.to_inner_uid(specified_userid)\n",
    "user_neighbors = algo.get_neighbors(user_inner_id, k=k)\n",
    "\n",
    "# Convert inner IDs back to the original user IDs\n",
    "user_neighbors = [algo.trainset.to_raw_uid(inner_id) for inner_id in user_neighbors]\n",
    "\n",
    "# Now, you have a list of user's nearest neighbors based on the trained model\n",
    "# You can recommend books from these nearest neighbors' rated books\n",
    "recommended_books = []\n",
    "\n",
    "for neighbor_userid in user_neighbors:\n",
    "    # Assuming you have a DataFrame 'all_ratings' with columns 'userId', 'bookTitle', and 'bookRating'\n",
    "    neighbor_ratings = all_ratings[all_ratings[\"userId\"] == neighbor_userid]\n",
    "\n",
    "    # Filter out books that the specified user has already rated\n",
    "    # already_read_list = list(all_ratings[all_ratings['userId'] == specified_userid]['bookTitle'])\n",
    "    neighbor_ratings = neighbor_ratings[~neighbor_ratings[\"bookTitle\"].isin([already_read_list])] \n",
    "    \n",
    "\n",
    "# Sort by bookRating in descending order to recommend the highest-rated books\n",
    "neighbor_ratings = neighbor_ratings.sort_values(by=\"bookRating\", ascending=False)\n",
    "\n",
    "# take the top-rated books from each neighbor and add them to the recommended_books list\n",
    "top_n_books = neighbor_ratings.head(num_recs)  # You can change the number of recommendations as needed\n",
    "recommended_books = top_n_books[\"bookTitle\"].tolist()\n",
    "\n",
    "# 'recommended_books' now contains the recommended books for the specified user based on their nearest neighbors' preferences\n",
    "print(\"Recommended books for user\", specified_userid, \":\")\n",
    "print(recommended_books)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['The Poisonwood Bible: A Novel',\n",
      " 'Deception on His Mind',\n",
      " \"Complications: A Surgeon's Notes on an Imperfect Science\",\n",
      " 'The Other Boleyn Girl',\n",
      " 'Mortal Sins',\n",
      " 'The Poyson Garden: An Elizabethan I Mystery (Elizabeth I Mysteries '\n",
      " '(Paperback))',\n",
      " \"Help I'm a Parent\",\n",
      " 'Dark Nantucket Noon: A Homer Kelly Mystery',\n",
      " \"The Quilter's Apprentice\",\n",
      " 'The English Assassin']\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "pprint(recommended_books)"
   ]
  }
 ],
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