19-Movie-Recommendation-System-V2/Movie_Recommendation_System_V2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f0b16e9",
   "metadata": {},
   "source": [
    "# Movie Recommendation System V2\n",
    "Advance system with more logical thinking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a27c5a8",
   "metadata": {},
   "source": [
    "### Import all dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0e43a570",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93eea69",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "73f38921",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load movies dataset\n",
    "movies = pd.read_csv(\"data/movies.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9da2ce59",
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62418</th>\n",
       "      <td>209157</td>\n",
       "      <td>We (2018)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62419</th>\n",
       "      <td>209159</td>\n",
       "      <td>Window of the Soul (2001)</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62420</th>\n",
       "      <td>209163</td>\n",
       "      <td>Bad Poems (2018)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62421</th>\n",
       "      <td>209169</td>\n",
       "      <td>A Girl Thing (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62422</th>\n",
       "      <td>209171</td>\n",
       "      <td>Women of Devil's Island (1962)</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62423 rows × 3 columns</p>\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",
       "62418   209157                           We (2018)   \n",
       "62419   209159           Window of the Soul (2001)   \n",
       "62420   209163                    Bad Poems (2018)   \n",
       "62421   209169                 A Girl Thing (2001)   \n",
       "62422   209171      Women of Devil's Island (1962)   \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  \n",
       "...                                            ...  \n",
       "62418                                        Drama  \n",
       "62419                                  Documentary  \n",
       "62420                                 Comedy|Drama  \n",
       "62421                           (no genres listed)  \n",
       "62422                       Action|Adventure|Drama  \n",
       "\n",
       "[62423 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display movies dataset\n",
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f9c3cea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# text clean function\n",
    "def clean_title(title):\n",
    "    return re.sub(\"[^a-zA-Z0-9 ]\", \"\", title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0246338b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column that contain clean title\n",
    "movies[\"clean_title\"] = movies[\"title\"].apply(clean_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c4b8d6b5",
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>clean_title</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",
       "      <td>Toy Story 1995</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",
       "      <td>Jumanji 1995</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",
       "      <td>Grumpier Old Men 1995</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",
       "      <td>Waiting to Exhale 1995</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",
       "      <td>Father of the Bride Part II 1995</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>62418</th>\n",
       "      <td>209157</td>\n",
       "      <td>We (2018)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>We 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62419</th>\n",
       "      <td>209159</td>\n",
       "      <td>Window of the Soul (2001)</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Window of the Soul 2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62420</th>\n",
       "      <td>209163</td>\n",
       "      <td>Bad Poems (2018)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>Bad Poems 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62421</th>\n",
       "      <td>209169</td>\n",
       "      <td>A Girl Thing (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>A Girl Thing 2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62422</th>\n",
       "      <td>209171</td>\n",
       "      <td>Women of Devil's Island (1962)</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "      <td>Women of Devils Island 1962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62423 rows × 4 columns</p>\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",
       "62418   209157                           We (2018)   \n",
       "62419   209159           Window of the Soul (2001)   \n",
       "62420   209163                    Bad Poems (2018)   \n",
       "62421   209169                 A Girl Thing (2001)   \n",
       "62422   209171      Women of Devil's Island (1962)   \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   \n",
       "...                                            ...   \n",
       "62418                                        Drama   \n",
       "62419                                  Documentary   \n",
       "62420                                 Comedy|Drama   \n",
       "62421                           (no genres listed)   \n",
       "62422                       Action|Adventure|Drama   \n",
       "\n",
       "                            clean_title  \n",
       "0                        Toy Story 1995  \n",
       "1                          Jumanji 1995  \n",
       "2                 Grumpier Old Men 1995  \n",
       "3                Waiting to Exhale 1995  \n",
       "4      Father of the Bride Part II 1995  \n",
       "...                                 ...  \n",
       "62418                           We 2018  \n",
       "62419           Window of the Soul 2001  \n",
       "62420                    Bad Poems 2018  \n",
       "62421                 A Girl Thing 2001  \n",
       "62422       Women of Devils Island 1962  \n",
       "\n",
       "[62423 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3745bc0e",
   "metadata": {},
   "source": [
    "### Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7c182ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a tfidf matrix\n",
    "vectorizer = TfidfVectorizer(ngram_range = (1,2))\n",
    "tfidf = vectorizer.fit_transform(movies[\"clean_title\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b17f4913",
   "metadata": {},
   "source": [
    "### Search Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e8231387",
   "metadata": {},
   "outputs": [],
   "source": [
    "def search(title):\n",
    "    title = clean_title(title)\n",
    "    query_vec = vectorizer.transform([title])\n",
    "    similarity = cosine_similarity(query_vec, tfidf).flatten()\n",
    "    indices = np.argpartition(similarity, -5)[-5:]\n",
    "    results = movies.iloc[indices][::-1]\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a677c8",
   "metadata": {},
   "source": [
    "### Search Box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3916e009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8ca2602c0c54c179f0187138139ccf3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='Toy Story', description='Movie Title: ')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5db8e7be7274d43a832419656c2c3ae",
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# input label and box\n",
    "movie_input = widgets.Text(\n",
    "    value = \"Toy Story\",\n",
    "    description = \"Movie Title: \",\n",
    "    desabled = False\n",
    ")\n",
    "\n",
    "# show search results\n",
    "movie_list = widgets.Output()\n",
    "\n",
    "# when type this will be called \n",
    "def on_type(data):\n",
    "    with movie_list:\n",
    "        movie_list.clear_output()\n",
    "        display(data)\n",
    "        title = data[\"new\"]\n",
    "        if len(title) > 5:\n",
    "            display(search(title))\n",
    "\n",
    "# hook the ontype func with input\n",
    "movie_input.observe(on_type, names = \"value\")\n",
    "\n",
    "# show input and output\n",
    "display(movie_input, movie_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5bbaa3",
   "metadata": {},
   "source": [
    "## Making more logical recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f03c01",
   "metadata": {},
   "source": [
    "### Movie Ratings Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "09bda329",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load ratings dataset\n",
    "ratings = pd.read_csv(\"data/ratings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "57668875",
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>1147880044</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>3.5</td>\n",
       "      <td>1147868817</td>\n",
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       "      <th>2</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>1147878820</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3.5</td>\n",
       "      <td>1147868510</td>\n",
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       "      <td>4.5</td>\n",
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       "      <th>25000091</th>\n",
       "      <td>162541</td>\n",
       "      <td>55768</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1240951998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000092</th>\n",
       "      <td>162541</td>\n",
       "      <td>56176</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1240950697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000093</th>\n",
       "      <td>162541</td>\n",
       "      <td>58559</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1240953434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000094</th>\n",
       "      <td>162541</td>\n",
       "      <td>63876</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1240952515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000095 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating   timestamp\n",
       "0              1      296     5.0  1147880044\n",
       "1              1      306     3.5  1147868817\n",
       "2              1      307     5.0  1147868828\n",
       "3              1      665     5.0  1147878820\n",
       "4              1      899     3.5  1147868510\n",
       "...          ...      ...     ...         ...\n",
       "25000090  162541    50872     4.5  1240953372\n",
       "25000091  162541    55768     2.5  1240951998\n",
       "25000092  162541    56176     2.0  1240950697\n",
       "25000093  162541    58559     4.0  1240953434\n",
       "25000094  162541    63876     5.0  1240952515\n",
       "\n",
       "[25000095 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display ratings dataset\n",
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "15f21c72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId         int64\n",
       "movieId        int64\n",
       "rating       float64\n",
       "timestamp      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show datatypes of ratings dataset\n",
    "ratings.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5700581e",
   "metadata": {},
   "source": [
    "### Find Similar Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "effc8ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_id = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1e8f6f20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    18,     75,    105, ..., 162388, 162407, 162510], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anyone who watched and rated that movie avobe 4\n",
    "similar_users = ratings[(ratings[\"movieId\"] == movie_id) & (ratings[\"rating\"] >= 4)][\"userId\"].unique()\n",
    "similar_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7e6f45d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3018            5\n",
       "3019            7\n",
       "3020           10\n",
       "3021           11\n",
       "3023           19\n",
       "            ...  \n",
       "24991092    33794\n",
       "24991093    44191\n",
       "24991094    45722\n",
       "24991095    48516\n",
       "24991096    49272\n",
       "Name: movieId, Length: 362923, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get ids of other movies they liked and rated above 4\n",
    "similar_user_recs = ratings[(ratings[\"userId\"].isin(similar_users)) & (ratings[\"rating\"] >= 4)][\"movieId\"]\n",
    "similar_user_recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ef596384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "5         3161\n",
       "356       1513\n",
       "1         1511\n",
       "780       1412\n",
       "62        1271\n",
       "          ... \n",
       "72601        1\n",
       "86286        1\n",
       "94005        1\n",
       "108575       1\n",
       "3804         1\n",
       "Name: count, Length: 13097, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# times each movie appears in dataset\n",
    "similar_user_recs = similar_user_recs.value_counts()\n",
    "similar_user_recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "87be6206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count\n",
       "1    1.316356\n",
       "2    0.499209\n",
       "3    0.296109\n",
       "4    0.200886\n",
       "5    0.150902\n",
       "7    0.109143\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert into %\n",
    "similar_user_recs = similar_user_recs.value_counts() / len(similar_users)\n",
    "\n",
    "# take the movies that appears more (> 10%)\n",
    "similar_user_recs = similar_user_recs[similar_user_recs > .1]\n",
    "similar_user_recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "96189a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>859383142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>943135696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5101</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>857131378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7276</th>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1337179995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9939</th>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1537207651</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>24996419</th>\n",
       "      <td>162519</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1000946439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997459</th>\n",
       "      <td>162524</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1072919304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997758</th>\n",
       "      <td>162527</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1301688215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998300</th>\n",
       "      <td>162530</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>989808332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998525</th>\n",
       "      <td>162533</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1280920369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25413 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating   timestamp\n",
       "1459           9        2     5.0   859383142\n",
       "3864          23        3     5.0   943135696\n",
       "5101          36        1     5.0   857131378\n",
       "7276          58        3     5.0  1337179995\n",
       "9939          75        1     5.0  1537207651\n",
       "...          ...      ...     ...         ...\n",
       "24996419  162519        1     5.0  1000946439\n",
       "24997459  162524        1     4.5  1072919304\n",
       "24997758  162527        1     4.5  1301688215\n",
       "24998300  162530        1     5.0   989808332\n",
       "24998525  162533        1     4.5  1280920369\n",
       "\n",
       "[25413 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how much popular these movies are (all people liked)\n",
    "all_users = ratings[(ratings[\"movieId\"].isin(similar_user_recs.index)) & (ratings[\"rating\"] > 4)]\n",
    "all_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1b4c06f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1    0.822094\n",
       "2    0.116407\n",
       "7    0.069748\n",
       "3    0.050762\n",
       "5    0.042250\n",
       "4    0.007944\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert into % \n",
    "all_users_recs = all_users[\"movieId\"].value_counts() / len(all_users[\"userId\"].unique())\n",
    "all_users_recs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "315f23bd",
   "metadata": {},
   "source": [
    "### Create Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "163ec288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\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>similar</th>\n",
       "      <th>all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.316356</td>\n",
       "      <td>0.822094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.499209</td>\n",
       "      <td>0.116407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.296109</td>\n",
       "      <td>0.050762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.200886</td>\n",
       "      <td>0.007944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.150902</td>\n",
       "      <td>0.042250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.109143</td>\n",
       "      <td>0.069748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    similar       all\n",
       "1  1.316356  0.822094\n",
       "2  0.499209  0.116407\n",
       "3  0.296109  0.050762\n",
       "4  0.200886  0.007944\n",
       "5  0.150902  0.042250\n",
       "7  0.109143  0.069748"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how much similar users liked them and all users liked them\n",
    "rec_percentages = pd.concat([similar_user_recs, all_users_recs], axis = 1)\n",
    "rec_percentages.columns = [\"similar\", \"all\"]\n",
    "rec_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e9779010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>similar</th>\n",
       "      <th>all</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.200886</td>\n",
       "      <td>0.007944</td>\n",
       "      <td>25.288431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.296109</td>\n",
       "      <td>0.050762</td>\n",
       "      <td>5.833318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.499209</td>\n",
       "      <td>0.116407</td>\n",
       "      <td>4.288481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.150902</td>\n",
       "      <td>0.042250</td>\n",
       "      <td>3.571598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.316356</td>\n",
       "      <td>0.822094</td>\n",
       "      <td>1.601222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.109143</td>\n",
       "      <td>0.069748</td>\n",
       "      <td>1.564811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    similar       all      score\n",
       "4  0.200886  0.007944  25.288431\n",
       "3  0.296109  0.050762   5.833318\n",
       "2  0.499209  0.116407   4.288481\n",
       "5  0.150902  0.042250   3.571598\n",
       "1  1.316356  0.822094   1.601222\n",
       "7  0.109143  0.069748   1.564811"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ratio similar and all users liked movies \n",
    "rec_percentages[\"score\"] = rec_percentages[\"similar\"] / rec_percentages[\"all\"]\n",
    "\n",
    "# sort all the values\n",
    "rec_percentages = rec_percentages.sort_values(\"score\", ascending = False)\n",
    "\n",
    "rec_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b26d2e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>similar</th>\n",
       "      <th>all</th>\n",
       "      <th>score</th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>clean_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.200886</td>\n",
       "      <td>0.007944</td>\n",
       "      <td>25.288431</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>Waiting to Exhale 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.296109</td>\n",
       "      <td>0.050762</td>\n",
       "      <td>5.833318</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Grumpier Old Men 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.499209</td>\n",
       "      <td>0.116407</td>\n",
       "      <td>4.288481</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>Jumanji 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.150902</td>\n",
       "      <td>0.042250</td>\n",
       "      <td>3.571598</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Father of the Bride Part II 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.316356</td>\n",
       "      <td>0.822094</td>\n",
       "      <td>1.601222</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>Toy Story 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.109143</td>\n",
       "      <td>0.069748</td>\n",
       "      <td>1.564811</td>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Sabrina 1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    similar       all      score  movieId                               title  \\\n",
       "3  0.200886  0.007944  25.288431        4            Waiting to Exhale (1995)   \n",
       "2  0.296109  0.050762   5.833318        3             Grumpier Old Men (1995)   \n",
       "1  0.499209  0.116407   4.288481        2                      Jumanji (1995)   \n",
       "4  0.150902  0.042250   3.571598        5  Father of the Bride Part II (1995)   \n",
       "0  1.316356  0.822094   1.601222        1                    Toy Story (1995)   \n",
       "6  0.109143  0.069748   1.564811        7                      Sabrina (1995)   \n",
       "\n",
       "                                        genres  \\\n",
       "3                         Comedy|Drama|Romance   \n",
       "2                               Comedy|Romance   \n",
       "1                   Adventure|Children|Fantasy   \n",
       "4                                       Comedy   \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "6                               Comedy|Romance   \n",
       "\n",
       "                        clean_title  \n",
       "3            Waiting to Exhale 1995  \n",
       "2             Grumpier Old Men 1995  \n",
       "1                      Jumanji 1995  \n",
       "4  Father of the Bride Part II 1995  \n",
       "0                    Toy Story 1995  \n",
       "6                      Sabrina 1995  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take top 10 recommendations and add them to movies data to get the titles\n",
    "rec_percentages.head(10).merge(movies, left_index = True, right_on = \"movieId\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac03e5c1",
   "metadata": {},
   "source": [
    "### Putting all things together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c101e3de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# recommendation function\n",
    "def find_similar_movies(movie_id):\n",
    "    # anyone who watched and rated that movie avobe 4\n",
    "    similar_users = ratings[(ratings[\"movieId\"] == movie_id) & (ratings[\"rating\"] >= 4)][\"userId\"].unique()\n",
    "    # get ids of other movies they liked and rated above 4\n",
    "    similar_user_recs = ratings[(ratings[\"userId\"].isin(similar_users)) & (ratings[\"rating\"] >= 4)][\"movieId\"]\n",
    "    \n",
    "    # convert into %\n",
    "    similar_user_recs = similar_user_recs.value_counts() / len(similar_users)\n",
    "    # take the movies that appears more (> 10%)\n",
    "    similar_user_recs = similar_user_recs[similar_user_recs > .1]\n",
    "    \n",
    "    # how much popular these movies are (all people liked)\n",
    "    all_users = ratings[(ratings[\"movieId\"].isin(similar_user_recs.index)) & (ratings[\"rating\"] > 4)]\n",
    "    # convert into % \n",
    "    all_users_recs = all_users[\"movieId\"].value_counts() / len(all_users[\"userId\"].unique())\n",
    "    \n",
    "    # how much similar users liked them and all users liked them\n",
    "    rec_percentages = pd.concat([similar_user_recs, all_users_recs], axis = 1)\n",
    "    rec_percentages.columns = [\"similar\", \"all\"]\n",
    "    \n",
    "    # ratio similar and all users liked movies \n",
    "    rec_percentages[\"score\"] = rec_percentages[\"similar\"] / rec_percentages[\"all\"]\n",
    "    \n",
    "    # sort all the values\n",
    "    rec_percentages = rec_percentages.sort_values(\"score\", ascending = False)\n",
    "    \n",
    "    # take top 10 recommendations and add them to movies data to get the titles\n",
    "    return rec_percentages.head(10).merge(movies, left_index = True, right_on = \"movieId\")[[\"score\", \"title\", \"genres\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8037af9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be68ba989f024c139783bdea1b6574ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='Toy Story', description='Movie Title:')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d05312a8c0f45869cb360920c5db53b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create widget to take input and show results and recommendations\n",
    "\n",
    "# input field and label\n",
    "movie_name_input = widgets.Text(\n",
    "    value = \"Toy Story\",\n",
    "    description = \"Movie Title:\",\n",
    "    disabled = False\n",
    ")\n",
    "\n",
    "# create output widget\n",
    "recommandation_list = widgets.Output()\n",
    "\n",
    "# function for output\n",
    "def on_type(data):\n",
    "    with recommandation_list:\n",
    "        recommandation_list.clear_output()\n",
    "        title = data[\"new\"]\n",
    "        if len(title) > 5:\n",
    "            results = search(title)\n",
    "            movie_id = results.iloc[0][\"movieId\"]\n",
    "            display(find_similar_movies(movie_id))\n",
    "\n",
    "# make search box active on type\n",
    "movie_name_input.observe(on_type, names = \"value\")\n",
    "\n",
    "# display the widgets\n",
    "display(movie_name_input, recommandation_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4974a1d",
   "metadata": {},
   "source": [
    "### Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cb95b295",
   "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>score</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8614</th>\n",
       "      <td>1541.533981</td>\n",
       "      <td>Batman (1966)</td>\n",
       "      <td>Action|Adventure|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5741</th>\n",
       "      <td>111.649168</td>\n",
       "      <td>Scanners (1981)</td>\n",
       "      <td>Horror|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3120</th>\n",
       "      <td>93.492316</td>\n",
       "      <td>Batman: Mask of the Phantasm (1993)</td>\n",
       "      <td>Animation|Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7358</th>\n",
       "      <td>91.567474</td>\n",
       "      <td>Octopussy (1983)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2525</th>\n",
       "      <td>90.442064</td>\n",
       "      <td>Dick Tracy (1990)</td>\n",
       "      <td>Action|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>90.121672</td>\n",
       "      <td>Batman &amp; Robin (1997)</td>\n",
       "      <td>Action|Adventure|Fantasy|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7122</th>\n",
       "      <td>75.650137</td>\n",
       "      <td>Chitty Chitty Bang Bang (1968)</td>\n",
       "      <td>Adventure|Children|Comedy|Fantasy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6072</th>\n",
       "      <td>73.586952</td>\n",
       "      <td>Man Who Fell to Earth, The (1976)</td>\n",
       "      <td>Drama|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>72.525602</td>\n",
       "      <td>Muppets Take Manhattan, The (1984)</td>\n",
       "      <td>Children|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3829</th>\n",
       "      <td>69.470199</td>\n",
       "      <td>Invisible Man, The (1933)</td>\n",
       "      <td>Horror|Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            score                                title  \\\n",
       "8614  1541.533981                        Batman (1966)   \n",
       "5741   111.649168                      Scanners (1981)   \n",
       "3120    93.492316  Batman: Mask of the Phantasm (1993)   \n",
       "7358    91.567474                     Octopussy (1983)   \n",
       "2525    90.442064                    Dick Tracy (1990)   \n",
       "1506    90.121672                Batman & Robin (1997)   \n",
       "7122    75.650137       Chitty Chitty Bang Bang (1968)   \n",
       "6072    73.586952    Man Who Fell to Earth, The (1976)   \n",
       "3303    72.525602   Muppets Take Manhattan, The (1984)   \n",
       "3829    69.470199            Invisible Man, The (1933)   \n",
       "\n",
       "                                         genres  \n",
       "8614                    Action|Adventure|Comedy  \n",
       "5741                     Horror|Sci-Fi|Thriller  \n",
       "3120                         Animation|Children  \n",
       "7358                  Action|Adventure|Thriller  \n",
       "2525                               Action|Crime  \n",
       "1506          Action|Adventure|Fantasy|Thriller  \n",
       "7122  Adventure|Children|Comedy|Fantasy|Musical  \n",
       "6072                               Drama|Sci-Fi  \n",
       "3303                    Children|Comedy|Musical  \n",
       "3829                              Horror|Sci-Fi  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# recommendation for \"Batman\" (just to show without running codes)\n",
    "results = search(\"Batman\")\n",
    "movie_id = results.iloc[0][\"movieId\"]\n",
    "display(find_similar_movies(movie_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8bc2628",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08f49aa6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a78e0063",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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