Pratilipi_assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a4a08b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d741aa1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading dataset using only columns which are really important\n",
    "df_meta = pd.read_csv('metadata.csv', usecols=['author_id','pratilipi_id','category_name','reading_time'])\n",
    "df_user = pd.read_csv('user-interactions.csv', usecols= ['user_id', 'pratilipi_id', 'read_percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c01aca90",
   "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_id</th>\n",
       "      <th>pratilipi_id</th>\n",
       "      <th>read_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5506791963854965</td>\n",
       "      <td>1377786220672965</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5506791988747277</td>\n",
       "      <td>1377786224767880</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5506791984769052</td>\n",
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       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5506791980825783</td>\n",
       "      <td>1377786227076616</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id      pratilipi_id  read_percent\n",
       "0  5506791963854965  1377786220672965         100.0\n",
       "3  5506791988747277  1377786224767880         100.0\n",
       "4  5506791992372558  1377786218111595         100.0\n",
       "5  5506791984769052  1377786221730720         100.0\n",
       "6  5506791980825783  1377786227076616         100.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61f5f8ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#considring famous books only since datset is already too large\n",
    "df_meta = df_meta.loc[df_meta['reading_time']>=1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29c04851",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_meta = df_meta.sort_values(by='reading_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dabfb3be",
   "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>author_id</th>\n",
       "      <th>pratilipi_id</th>\n",
       "      <th>category_name</th>\n",
       "      <th>reading_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>516481</th>\n",
       "      <td>-2270332332743399</td>\n",
       "      <td>1377786226671520</td>\n",
       "      <td>moral-inspiring</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396105</th>\n",
       "      <td>-2270332334418083</td>\n",
       "      <td>1377786224409031</td>\n",
       "      <td>romance</td>\n",
       "      <td>1000</td>\n",
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       "    <tr>\n",
       "      <th>218775</th>\n",
       "      <td>-2270332345997468</td>\n",
       "      <td>1377786223507765</td>\n",
       "      <td>family</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331591</th>\n",
       "      <td>-3428527958586737</td>\n",
       "      <td>1377786220003449</td>\n",
       "      <td>romance</td>\n",
       "      <td>1000</td>\n",
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       "    <tr>\n",
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       "      <td>-2270332326956164</td>\n",
       "      <td>1377786226193497</td>\n",
       "      <td>romance</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               author_id      pratilipi_id    category_name  reading_time\n",
       "516481 -2270332332743399  1377786226671520  moral-inspiring          1000\n",
       "396105 -2270332334418083  1377786224409031          romance          1000\n",
       "218775 -2270332345997468  1377786223507765           family          1000\n",
       "331591 -3428527958586737  1377786220003449          romance          1000\n",
       "429862 -2270332326956164  1377786226193497          romance          1000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5d02df55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "romance                      11148\n",
       "novels                        5691\n",
       "suspense                      5048\n",
       "social                        3170\n",
       "family                        2740\n",
       "action-and-adventure          1671\n",
       "women                         1531\n",
       "horror                        1367\n",
       "entertainment                 1101\n",
       "shortstories                  1082\n",
       "life                          1035\n",
       "moral-inspiring                996\n",
       "webseries                      672\n",
       "Pratilipi-kalamkar-samman      572\n",
       "fantasy                        499\n",
       "comedy                         451\n",
       "mythology                      441\n",
       "Pratilipi-Awards-Hindi         439\n",
       "Indiawale                      389\n",
       "drama                          343\n",
       "experiences-and-memories       306\n",
       "crime                          278\n",
       "relegion-and-spiritual         271\n",
       "politics                       205\n",
       "murder-mystery                 194\n",
       "translation                    183\n",
       "swahindi2                      161\n",
       "science-fiction                158\n",
       "detective                      153\n",
       "crime-lekhan                   150\n",
       "children                       107\n",
       "Horror-Marathon                 93\n",
       "Serieswriting                   80\n",
       "premkamahina                    63\n",
       "The-Chat-Story                  45\n",
       "Parytan                         36\n",
       "krishi-jeevan                   30\n",
       "short-story-challenge           29\n",
       "pratilipi-kids                  17\n",
       "pravasi-sahitya                 17\n",
       "Radio-Fiction                   17\n",
       "health-and-wellness             17\n",
       "cyber-crime-fiction             11\n",
       "erotica                          8\n",
       "Rashtriya                        5\n",
       "Name: category_name, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.category_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3aff25d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='category_name', data = df_meta, palette='pastel')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9baa32ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#considering user only who read the entire book\n",
    "df_user = df_user.loc[df_user['read_percent']==100.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32eeecc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8702791, 3), (43020, 4))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user.shape, df_meta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ce5f2413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1229343, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging both the dataset\n",
    "df = df_meta.merge(df_user, on='pratilipi_id', how = 'inner')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6b099496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pratilipi_id</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-862730317031736</td>\n",
       "      <td>social</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-844691134572856</td>\n",
       "      <td>shortstories suspense horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-843753303402808</td>\n",
       "      <td>social romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-821302225365304</td>\n",
       "      <td>romance social</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-805561288190264</td>\n",
       "      <td>social</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-793460789831992</td>\n",
       "      <td>romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-774778583869752</td>\n",
       "      <td>social</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-731837801261368</td>\n",
       "      <td>romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-727142821095736</td>\n",
       "      <td>shortstories romance moral-inspiring</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pratilipi_id                         category_name\n",
       "0 -868854658328888                              suspense\n",
       "1 -862730317031736                                social\n",
       "2 -844691134572856          shortstories suspense horror\n",
       "3 -843753303402808                        social romance\n",
       "4 -821302225365304                        romance social\n",
       "5 -805561288190264                                social\n",
       "6 -793460789831992                               romance\n",
       "7 -774778583869752                                social\n",
       "8 -731837801261368                               romance\n",
       "9 -727142821095736  shortstories romance moral-inspiring"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#since most of pratilipies have different categories at different row that's why grouping them and joining the categories\n",
    "#this is for content based recommendations in our use case category_name based recommendations\n",
    "temp = df.groupby('pratilipi_id').agg({'category_name': lambda x: ' '.join(set(x))}).reset_index()\n",
    "temp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9c7482d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pratilipi_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791955529201</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791993547868</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791995219032</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791995230274</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791995256459</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791995295105</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791995316845</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791995320587</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791995322336</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-868854658328888</td>\n",
       "      <td>5506791995354618</td>\n",
       "      <td>-2882685197546865</td>\n",
       "      <td>suspense</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pratilipi_id           user_id         author_id category_name\n",
       "0 -868854658328888  5506791955529201 -2882685197546865      suspense\n",
       "1 -868854658328888  5506791993547868 -2882685197546865      suspense\n",
       "2 -868854658328888  5506791995219032 -2882685197546865      suspense\n",
       "3 -868854658328888  5506791995230274 -2882685197546865      suspense\n",
       "4 -868854658328888  5506791995256459 -2882685197546865      suspense\n",
       "5 -868854658328888  5506791995295105 -2882685197546865      suspense\n",
       "6 -868854658328888  5506791995316845 -2882685197546865      suspense\n",
       "7 -868854658328888  5506791995320587 -2882685197546865      suspense\n",
       "8 -868854658328888  5506791995322336 -2882685197546865      suspense\n",
       "9 -868854658328888  5506791995354618 -2882685197546865      suspense"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating another dataframe for collabrative filtering recommendations here we are considering users \n",
    "temp2 = df.groupby(['pratilipi_id', 'user_id', 'author_id']).agg({'category_name': lambda x: ' '.join(set(x))}).reset_index()\n",
    "temp2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ff6a9a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#choosing first 100k rows\n",
    "tm_df = temp.loc[:100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4bbe21ae",
   "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>pratilipi_id</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11940</th>\n",
       "      <td>1377786228056119</td>\n",
       "      <td>Indiawale romance suspense</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pratilipi_id               category_name\n",
       "11940  1377786228056119  Indiawale romance suspense"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm_df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "42a56b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#count vectorerizer to create vector\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_features=50,stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f009e696",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = cv.fit_transform(tm_df['category_name']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "baef2928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0d99ec65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cosine similarity to get similarity between vectors\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b498a771",
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = cosine_similarity(vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "86f8cc62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.        , 0.57735027, ..., 0.        , 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "117ad65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to recoomend based on books a user watch category_based filtering \n",
    "def recommend(book_id):\n",
    "    tem_lst = []\n",
    "    index = tm_df[tm_df['pratilipi_id'] == book_id].index[0]\n",
    "    distances = sorted(list(enumerate(similarity[index])),reverse=True,key = lambda x: x[1])\n",
    "    for i in distances[1:6]:\n",
    "        tem_lst.append(tm_df.iloc[i[0]].pratilipi_id)\n",
    "    return tem_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ab329331",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1377786224559030, 1377786224824649, 1377786225003004, 1377786225526545, 1377786225607351]\n"
     ]
    }
   ],
   "source": [
    "rec_lst = recommend(1377786228056119)\n",
    "print(rec_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "71c648d0",
   "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>pratilipi_id</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8185</th>\n",
       "      <td>1377786224559030</td>\n",
       "      <td>Indiawale suspense romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8484</th>\n",
       "      <td>1377786224824649</td>\n",
       "      <td>Indiawale romance suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8676</th>\n",
       "      <td>1377786225003004</td>\n",
       "      <td>Indiawale romance suspense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9310</th>\n",
       "      <td>1377786225526545</td>\n",
       "      <td>Indiawale suspense romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9421</th>\n",
       "      <td>1377786225607351</td>\n",
       "      <td>Indiawale suspense romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pratilipi_id               category_name\n",
       "8185  1377786224559030  Indiawale suspense romance\n",
       "8484  1377786224824649  Indiawale romance suspense\n",
       "8676  1377786225003004  Indiawale romance suspense\n",
       "9310  1377786225526545  Indiawale suspense romance\n",
       "9421  1377786225607351  Indiawale suspense romance"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#recommended pratilipies dataframe for better understanding \n",
    "tm_df[tm_df['pratilipi_id'].isin(rec_lst)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "deeebce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#considering 100k rows since the dataset is too large to computer similarity\n",
    "tm_df2 = temp2.loc[:30000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e9bf9d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector2 = cv.fit_transform(tm_df2['category_name']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f19b54a",
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = cosine_similarity(vector2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5b79cc18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30001, 30001)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8c4a7a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to recommend pratilipies based on similar users read pratilipies.   \n",
    "def recommend2(user_id):\n",
    "    usr_lst= []\n",
    "    book_lst = set()\n",
    "    index = tm_df2[tm_df2['user_id'] == user_id].index[0]\n",
    "    distances = sorted(list(enumerate(similarity[index])),reverse=True,key = lambda x: x[1])\n",
    "    for i in distances[1:]:\n",
    "        if len(book_lst) < 5:\n",
    "            sim_users = tm_df2.iloc[i[0]].user_id\n",
    "            usr_lst.append(sim_users)\n",
    "            for j in usr_lst:\n",
    "                k = tm_df2[tm_df2['user_id']==j]['pratilipi_id'].values[0]\n",
    "                book_lst.add(k)\n",
    "    return list(book_lst)\n",
    "#     print(usr_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a4f7cb23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>pratilipi_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>18912</th>\n",
       "      <td>1377786216331554</td>\n",
       "      <td>5506791964756353</td>\n",
       "      <td>-2270332348764612</td>\n",
       "      <td>The-Chat-Story Parytan romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pratilipi_id           user_id         author_id  \\\n",
       "18912  1377786216331554  5506791964756353 -2270332348764612   \n",
       "\n",
       "                        category_name  \n",
       "18912  The-Chat-Story Parytan romance  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm_df2.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c19fe9ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1377786216331554, 1114522134943432, 979877554722504, 1377786215757175, 1377786216312666]\n"
     ]
    }
   ],
   "source": [
    "rec_lst2 = recommend2(5506791964756353)\n",
    "print(rec_lst2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "062a735d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>pratilipi_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6639</th>\n",
       "      <td>979877554722504</td>\n",
       "      <td>3280684228977316</td>\n",
       "      <td>-3678460257170801</td>\n",
       "      <td>social romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6640</th>\n",
       "      <td>979877554722504</td>\n",
       "      <td>3332846191551140</td>\n",
       "      <td>-3678460257170801</td>\n",
       "      <td>social romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6641</th>\n",
       "      <td>979877554722504</td>\n",
       "      <td>3453645753852580</td>\n",
       "      <td>-3678460257170801</td>\n",
       "      <td>social romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6642</th>\n",
       "      <td>979877554722504</td>\n",
       "      <td>3523322783376036</td>\n",
       "      <td>-3678460257170801</td>\n",
       "      <td>social romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6643</th>\n",
       "      <td>979877554722504</td>\n",
       "      <td>3595524319388324</td>\n",
       "      <td>-3678460257170801</td>\n",
       "      <td>social romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19256</th>\n",
       "      <td>1377786216331554</td>\n",
       "      <td>5506791996446109</td>\n",
       "      <td>-2270332348764612</td>\n",
       "      <td>The-Chat-Story Parytan romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19257</th>\n",
       "      <td>1377786216331554</td>\n",
       "      <td>5506791996452498</td>\n",
       "      <td>-2270332348764612</td>\n",
       "      <td>The-Chat-Story Parytan romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19258</th>\n",
       "      <td>1377786216331554</td>\n",
       "      <td>5506791996488561</td>\n",
       "      <td>-2270332348764612</td>\n",
       "      <td>The-Chat-Story Parytan romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19259</th>\n",
       "      <td>1377786216331554</td>\n",
       "      <td>5506791996530857</td>\n",
       "      <td>-2270332348764612</td>\n",
       "      <td>The-Chat-Story Parytan romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19260</th>\n",
       "      <td>1377786216331554</td>\n",
       "      <td>5506791996671730</td>\n",
       "      <td>-2270332348764612</td>\n",
       "      <td>The-Chat-Story Parytan romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3237 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           pratilipi_id           user_id         author_id  \\\n",
       "6639    979877554722504  3280684228977316 -3678460257170801   \n",
       "6640    979877554722504  3332846191551140 -3678460257170801   \n",
       "6641    979877554722504  3453645753852580 -3678460257170801   \n",
       "6642    979877554722504  3523322783376036 -3678460257170801   \n",
       "6643    979877554722504  3595524319388324 -3678460257170801   \n",
       "...                 ...               ...               ...   \n",
       "19256  1377786216331554  5506791996446109 -2270332348764612   \n",
       "19257  1377786216331554  5506791996452498 -2270332348764612   \n",
       "19258  1377786216331554  5506791996488561 -2270332348764612   \n",
       "19259  1377786216331554  5506791996530857 -2270332348764612   \n",
       "19260  1377786216331554  5506791996671730 -2270332348764612   \n",
       "\n",
       "                        category_name  \n",
       "6639                   social romance  \n",
       "6640                   social romance  \n",
       "6641                   social romance  \n",
       "6642                   social romance  \n",
       "6643                   social romance  \n",
       "...                               ...  \n",
       "19256  The-Chat-Story Parytan romance  \n",
       "19257  The-Chat-Story Parytan romance  \n",
       "19258  The-Chat-Story Parytan romance  \n",
       "19259  The-Chat-Story Parytan romance  \n",
       "19260  The-Chat-Story Parytan romance  \n",
       "\n",
       "[3237 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm_df2[tm_df2['pratilipi_id'].isin(rec_lst2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bdff97f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44693d25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fddf1d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64463777",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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