Implimentation of Content-Based Recommended System.ipynb

{
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   "cell_type": "markdown",
   "id": "561333e3-9ea5-4c15-b49d-4cce26c5e898",
   "metadata": {},
   "source": [
    "# Implimentation of Content-Based Recommended System "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f9989af-935e-4e88-b8dc-282b4829eff0",
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    "tags": []
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   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4228509-8a86-4d59-ac21-199a3149a813",
   "metadata": {},
   "source": [
    "## 1- Import and Explore Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bd53f82-f906-472c-ac2e-015cb1c727bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "movies_df = pd.read_csv('data/movies.csv')\n",
    "ratings_df = pd.read_csv('data/ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c34fcd1-e8c7-4051-9073-9a6ab6ca3a4b",
   "metadata": {
    "tags": []
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5783ca40-e964-4617-924e-f2936a28e9ea",
   "metadata": {
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "  </thead>\n",
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       "   userId  movieId  rating   timestamp\n",
       "0       1      169     2.5  1204927694\n",
       "1       1     2471     3.0  1204927438\n",
       "2       1    48516     5.0  1204927435\n",
       "3       2     2571     3.5  1436165433\n",
       "4       2   109487     4.0  1436165496"
      ]
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "ratings_df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "id": "f51f1b27-8d7f-4da2-ab8c-1cd45bd463c5",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34208, 3)\n",
      "----------------------------------------------------------------------\n",
      "(22884377, 4)\n"
     ]
    }
   ],
   "source": [
    "print(movies_df.shape)\n",
    "print('-'*70)\n",
    "print(ratings_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "505134b2-4769-4269-b832-6813712003af",
   "metadata": {
    "tags": []
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    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34208 entries, 0 to 34207\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   movieId  34208 non-null  int64 \n",
      " 1   title    34208 non-null  object\n",
      " 2   genres   34208 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 801.9+ KB\n",
      "None\n",
      "----------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22884377 entries, 0 to 22884376\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Dtype  \n",
      "---  ------     -----  \n",
      " 0   userId     int64  \n",
      " 1   movieId    int64  \n",
      " 2   rating     float64\n",
      " 3   timestamp  int64  \n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 698.4 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(movies_df.info())\n",
    "print('-'*70)\n",
    "print(ratings_df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ab9f15-583f-4756-b044-cae630f01c58",
   "metadata": {},
   "source": [
    "## Data Preparation and Pre-Processing :\n",
    "\n",
    "So each movie has a unique ID, a title with its release year along with it (Which may contain unicode characters) and several different genres in the same field. Let's remove the year from the title column and place it into its own one by using the handy [extract](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.extract.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01#pandas.Series.str.extract) function that Pandas has."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "399bcc20",
   "metadata": {},
   "source": [
    "Let's remove the year from the **title** column by using pandas' replace function and store it in a new **year** column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9173dbd-0cf8-49d9-a255-d3a5a6bfd625",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# extract the year of the film from the title\n",
    "movies_df['year'] = movies_df['title'].str.extract(r'\\((\\d{4})\\)', expand=False)\n",
    "movies_df['year'] = movies_df['title'].str.extract(r'(\\d{4})', expand=False)\n",
    "# movies_df['year'] = movies_df['title'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7825ee43-3125-4674-891c-c096c016dfe0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# remove the year of the film from the title\n",
    "movies_df['title'] = movies_df['title'].str.replace(r'\\(\\d{4}\\)', '', regex=True)\n",
    "movies_df['title'] = movies_df['title'].apply(lambda x : x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "336f2672-8c4e-4335-b36b-ad65ee83bf38",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1995</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Jumanji</td>\n",
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       "      <td>1995</td>\n",
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       "      <td>Waiting to Exhale</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>1995</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                        genres  year  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  1995  \n",
       "1                   Adventure|Children|Fantasy  1995  \n",
       "2                               Comedy|Romance  1995  \n",
       "3                         Comedy|Drama|Romance  1995  \n",
       "4                                       Comedy  1995  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84495585-e65d-491f-866a-e69fec1da238",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [Adventure, Animation, Children, Comedy, Fantasy]\n",
       "1                           [Adventure, Children, Fantasy]\n",
       "2                                        [Comedy, Romance]\n",
       "3                                 [Comedy, Drama, Romance]\n",
       "4                                                 [Comedy]\n",
       "                               ...                        \n",
       "34203                                           [Thriller]\n",
       "34204                                 [(no genres listed)]\n",
       "34205                                              [Drama]\n",
       "34206                                      [Drama, Sci-Fi]\n",
       "34207                                 [(no genres listed)]\n",
       "Name: genres, Length: 34208, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split the genres of the movie\n",
    "movies_df['genres'] = movies_df['genres'].str.split('|')\n",
    "movies_df['genres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "289631d5-635f-4ff8-9943-dfce6271f50a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# remove useless column\n",
    "ratings_df = ratings_df.drop('timestamp', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "896e2c11-abfe-4f19-833c-81ba6c314fe9",
   "metadata": {},
   "source": [
    "## Building the System :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d6901398-f673-4aa3-a057-3fcaabd5c7f6",
   "metadata": {
    "tags": []
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>1995</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                              genres  year  Adventure  \\\n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]  1995        1.0   \n",
       "1                     [Adventure, Children, Fantasy]  1995        1.0   \n",
       "2                                  [Comedy, Romance]  1995        0.0   \n",
       "3                           [Comedy, Drama, Romance]  1995        0.0   \n",
       "4                                           [Comedy]  1995        0.0   \n",
       "\n",
       "   Animation  Children  Comedy  Fantasy  Romance  ...  Horror  Mystery  \\\n",
       "0        1.0       1.0     1.0      1.0      0.0  ...     0.0      0.0   \n",
       "1        0.0       1.0     0.0      1.0      0.0  ...     0.0      0.0   \n",
       "2        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "3        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "4        0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "\n",
       "   Sci-Fi  IMAX  Documentary  War  Musical  Western  Film-Noir  \\\n",
       "0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "2     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "3     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "4     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "\n",
       "   (no genres listed)  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesWithGenres_df = movies_df.copy()\n",
    "\n",
    "# for every row in the dataframe, iterate trough the list of genres and put 1 for each corresponding column \n",
    "for index, row in movies_df.iterrows() :\n",
    "    for genre in row['genres'] :\n",
    "        moviesWithGenres_df.at[index, genre] = 1\n",
    "\n",
    "# fill the missing value with 0, to show that this film don't have this genre\n",
    "moviesWithGenres_df = moviesWithGenres_df.fillna(0)\n",
    "moviesWithGenres_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd91328b-c429-41a5-ae59-979c8157bb29",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breakfast Club, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jumanji</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akira</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 title  rating\n",
       "0  Breakfast Club, The     5.0\n",
       "1            Toy Story     3.5\n",
       "2              Jumanji     2.0\n",
       "3         Pulp Fiction     5.0\n",
       "4                Akira     4.5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define a input Movies for testing\n",
    "userInput = [\n",
    "         {'title':'Breakfast Club, The', 'rating':5},\n",
    "         {'title':'Toy Story', 'rating':3.5},\n",
    "         {'title':'Jumanji', 'rating':2},\n",
    "         {'title':\"Pulp Fiction\", 'rating':5},\n",
    "         {'title':'Akira', 'rating':4.5}\n",
    "   ] \n",
    "inputMovies = pd.DataFrame(userInput)\n",
    "inputMovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f14344e9-0157-41fa-971d-611ae59fca7c",
   "metadata": {
    "tags": []
   },
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1274</td>\n",
       "      <td>Akira</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1968</td>\n",
       "      <td>Breakfast Club, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                title  rating\n",
       "0        1            Toy Story     3.5\n",
       "1        2              Jumanji     2.0\n",
       "2      296         Pulp Fiction     5.0\n",
       "3     1274                Akira     4.5\n",
       "4     1968  Breakfast Club, The     5.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtering out the movies by title\n",
    "inputId = movies_df[movies_df['title'].isin(inputMovies['title'].tolist())]\n",
    "#Then merging it so we can get the movieId. It's implicitly merging it by title.\n",
    "inputMovies = pd.merge(inputId, inputMovies)\n",
    "#Dropping information we won't use from the input dataframe\n",
    "inputMovies = inputMovies.drop('year', axis=1).drop('genres', axis=1)\n",
    "inputMovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bfc5f30b-50fc-49b3-914c-976503ec4911",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Romance</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>IMAX</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>War</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Western</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>(no genres listed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>[Comedy, Crime, Drama, Thriller]</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>1274</td>\n",
       "      <td>Akira</td>\n",
       "      <td>[Action, Adventure, Animation, Sci-Fi]</td>\n",
       "      <td>1988</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>1968</td>\n",
       "      <td>Breakfast Club, The</td>\n",
       "      <td>[Comedy, Drama]</td>\n",
       "      <td>1985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                title  \\\n",
       "0           1            Toy Story   \n",
       "1           2              Jumanji   \n",
       "293       296         Pulp Fiction   \n",
       "1246     1274                Akira   \n",
       "1885     1968  Breakfast Club, The   \n",
       "\n",
       "                                                 genres  year  Adventure  \\\n",
       "0     [Adventure, Animation, Children, Comedy, Fantasy]  1995        1.0   \n",
       "1                        [Adventure, Children, Fantasy]  1995        1.0   \n",
       "293                    [Comedy, Crime, Drama, Thriller]  1994        0.0   \n",
       "1246             [Action, Adventure, Animation, Sci-Fi]  1988        1.0   \n",
       "1885                                    [Comedy, Drama]  1985        0.0   \n",
       "\n",
       "      Animation  Children  Comedy  Fantasy  Romance  ...  Horror  Mystery  \\\n",
       "0           1.0       1.0     1.0      1.0      0.0  ...     0.0      0.0   \n",
       "1           0.0       1.0     0.0      1.0      0.0  ...     0.0      0.0   \n",
       "293         0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "1246        1.0       0.0     0.0      0.0      0.0  ...     0.0      0.0   \n",
       "1885        0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "\n",
       "      Sci-Fi  IMAX  Documentary  War  Musical  Western  Film-Noir  \\\n",
       "0        0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1        0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "293      0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1246     1.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1885     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "\n",
       "      (no genres listed)  \n",
       "0                    0.0  \n",
       "1                    0.0  \n",
       "293                  0.0  \n",
       "1246                 0.0  \n",
       "1885                 0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userMovies = moviesWithGenres_df[moviesWithGenres_df['movieId'].isin(inputMovies['movieId'].tolist())]\n",
    "userMovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "019bc75c-ff38-4799-97e9-1558f62afc15",
   "metadata": {},
   "outputs": [],
   "source": [
    "userMovies = userMovies.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43716b96-1bd1-4d2c-bd05-e6155e434644",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Adventure', 'Animation', 'Children', 'Comedy', 'Fantasy', 'Romance',\n",
       "       'Drama', 'Action', 'Crime', 'Thriller', 'Horror', 'Mystery', 'Sci-Fi',\n",
       "       'IMAX', 'Documentary', 'War', 'Musical', 'Western', 'Film-Noir',\n",
       "       '(no genres listed)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep only the genres columns\n",
    "userGenreTable = userMovies.drop('year', axis=1).drop('genres', axis=1).drop('movieId', axis=1).drop('title', axis=1)\n",
    "userGenreTable.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f45d7f71-132f-44ad-abf1-48f59058cdea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.5\n",
       "1    2.0\n",
       "2    5.0\n",
       "3    4.5\n",
       "4    5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputMovies['rating']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0963e5",
   "metadata": {},
   "source": [
    "#### create a user profile to show the genres of movies he like more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f4b7f0b3-6804-4be4-9ab8-5027e5a2cbac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adventure             10.0\n",
       "Animation              8.0\n",
       "Children               5.5\n",
       "Comedy                13.5\n",
       "Fantasy                5.5\n",
       "Romance                0.0\n",
       "Drama                 10.0\n",
       "Action                 4.5\n",
       "Crime                  5.0\n",
       "Thriller               5.0\n",
       "Horror                 0.0\n",
       "Mystery                0.0\n",
       "Sci-Fi                 4.5\n",
       "IMAX                   0.0\n",
       "Documentary            0.0\n",
       "War                    0.0\n",
       "Musical                0.0\n",
       "Western                0.0\n",
       "Film-Noir              0.0\n",
       "(no genres listed)     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's create a user profile based on the rating in inputMovies \n",
    "userProfile = userGenreTable.transpose().dot(inputMovies['rating'])\n",
    "userProfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c07e85a-5531-4424-ba73-fb44f4de4af7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <th>Animation</th>\n",
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      "text/plain": [
       "         Adventure  Animation  Children  Comedy  Fantasy  Romance  Drama  \\\n",
       "movieId                                                                    \n",
       "1              1.0        1.0       1.0     1.0      1.0      0.0    0.0   \n",
       "2              1.0        0.0       1.0     0.0      1.0      0.0    0.0   \n",
       "3              0.0        0.0       0.0     1.0      0.0      1.0    0.0   \n",
       "4              0.0        0.0       0.0     1.0      0.0      1.0    1.0   \n",
       "5              0.0        0.0       0.0     1.0      0.0      0.0    0.0   \n",
       "\n",
       "         Action  Crime  Thriller  Horror  Mystery  Sci-Fi  IMAX  Documentary  \\\n",
       "movieId                                                                        \n",
       "1           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "2           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "3           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "4           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "5           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "\n",
       "         War  Musical  Western  Film-Noir  (no genres listed)  \n",
       "movieId                                                        \n",
       "1        0.0      0.0      0.0        0.0                 0.0  \n",
       "2        0.0      0.0      0.0        0.0                 0.0  \n",
       "3        0.0      0.0      0.0        0.0                 0.0  \n",
       "4        0.0      0.0      0.0        0.0                 0.0  \n",
       "5        0.0      0.0      0.0        0.0                 0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe that's contains each movie with its genres\n",
    "genreTable = moviesWithGenres_df.set_index(moviesWithGenres_df['movieId'])\n",
    "genreTable = genreTable.drop('year', axis=1).drop('genres', axis=1).drop('movieId', axis=1).drop('title', axis=1)\n",
    "genreTable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "51eaf4da-0341-4077-a936-23468272d407",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34208, 20)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genreTable.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c2ea39aa-7ef1-4f94-8066-e0a33f69a711",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1    0.594406\n",
       "2    0.293706\n",
       "3    0.188811\n",
       "4    0.328671\n",
       "5    0.188811\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a recommendation Table based on genreTable and userProfile\n",
    "recommendationTable_df = ((userProfile*genreTable).sum(axis=1))/(userProfile.sum())\n",
    "recommendationTable_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "177b99a5-0116-4b83-a7c4-55789c80eed2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                          Toy Story\n",
       "1                            Jumanji\n",
       "2                   Grumpier Old Men\n",
       "3                  Waiting to Exhale\n",
       "4        Father of the Bride Part II\n",
       "5                               Heat\n",
       "6                            Sabrina\n",
       "7                       Tom and Huck\n",
       "8                       Sudden Death\n",
       "9                          GoldenEye\n",
       "10           American President, The\n",
       "11       Dracula: Dead and Loving It\n",
       "12                             Balto\n",
       "13                             Nixon\n",
       "14                  Cutthroat Island\n",
       "15                            Casino\n",
       "16             Sense and Sensibility\n",
       "17                        Four Rooms\n",
       "18    Ace Ventura: When Nature Calls\n",
       "19                       Money Train\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we are able to get recommended movies \n",
    "recommender = movies_df.loc[movies_df['movieId'].isin(recommendationTable_df.head(20).keys())]\n",
    "recommender['title']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5734df44",
   "metadata": {},
   "source": [
    "### Advantages and Disadvantages of Content-Based Recommendation\n",
    "\n",
    "##### Advantages\n",
    "\n",
    "- **No Need for User Data:** Can recommend items even if there is limited or no information about other users (cold start problem for new users).\n",
    "- **Personalization:** Recommends items based on the individual user's preferences and history, providing highly personalized suggestions.\n",
    "- **No Privacy Concerns:** Doesn't rely on other users' data, reducing privacy issues.\n",
    "- **Adaptability:** Can adapt quickly when new content is added, as it only depends on the features of the item.\n",
    "\n",
    "##### Disadvantages\n",
    "\n",
    "- **Limited to Existing Preferences:** Can struggle to recommend items that differ from the user's past behavior, making it less effective in discovering new or diverse content.\n",
    "- **Feature Engineering Requirement:** Needs well-structured item data, requiring good feature extraction, which can be difficult for complex items (e.g., movies or music).\n",
    "- **Cold Start for New Items:** If the item lacks descriptive data (metadata), it may not be recommended effectively.\n",
    "- **Over-specialization:** May lead to a filter bubble, where the user is recommended only items similar to those already liked."
   ]
  }
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