movielens_collaborative_filtering_2.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#User-Item-Matrix\" data-toc-modified-id=\"User-Item-Matrix-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>User-Item Matrix</a></span><ul class=\"toc-item\"><li><span><a href=\"#Normalization\" data-toc-modified-id=\"Normalization-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Normalization</a></span></li><li><span><a href=\"#Similarity-Measure\" data-toc-modified-id=\"Similarity-Measure-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Similarity Measure</a></span><ul class=\"toc-item\"><li><span><a href=\"#Consine-Similarity\" data-toc-modified-id=\"Consine-Similarity-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>Consine Similarity</a></span><ul class=\"toc-item\"><li><span><a href=\"#Implementation\" data-toc-modified-id=\"Implementation-1.2.1.1\"><span class=\"toc-item-num\">1.2.1.1&nbsp;&nbsp;</span>Implementation</a></span></li><li><span><a href=\"#Using-sklearn.metrics.pairwise.cosine_similarity()\" data-toc-modified-id=\"Using-sklearn.metrics.pairwise.cosine_similarity()-1.2.1.2\"><span class=\"toc-item-num\">1.2.1.2&nbsp;&nbsp;</span>Using <code>sklearn.metrics.pairwise.cosine_similarity()</code></a></span></li></ul></li><li><span><a href=\"#Euclidean-Similarity\" data-toc-modified-id=\"Euclidean-Similarity-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Euclidean Similarity</a></span><ul class=\"toc-item\"><li><span><a href=\"#Implementation\" data-toc-modified-id=\"Implementation-1.2.2.1\"><span class=\"toc-item-num\">1.2.2.1&nbsp;&nbsp;</span>Implementation</a></span></li><li><span><a href=\"#Using-sklearn.metrics.pairwise.euclidean_distances()\" data-toc-modified-id=\"Using-sklearn.metrics.pairwise.euclidean_distances()-1.2.2.2\"><span class=\"toc-item-num\">1.2.2.2&nbsp;&nbsp;</span>Using <code>sklearn.metrics.pairwise.euclidean_distances()</code></a></span></li></ul></li></ul></li><li><span><a href=\"#Recommendation\" data-toc-modified-id=\"Recommendation-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Recommendation</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-16T08:57:17.636778Z",
     "start_time": "2022-01-16T08:57:12.592576Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from tensorflow.keras.utils import get_file\n",
    "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n",
    "from google_drive_downloader import GoogleDriveDownloader as gdd\n",
    "\n",
    "np.set_printoptions(precision=3)\n",
    "plt.style.use(\"dark_background\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-16T08:57:26.953759Z",
     "start_time": "2022-01-16T08:57:17.671758Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "movies = pd.read_csv(\"./Datasets/MovieLens/movies.csv\", index_col=0)\n",
    "genres = pd.read_csv(\"./Datasets/MovieLens/genres.csv\", index_col=0)\n",
    "gdd.download_file_from_google_drive(file_id=\"15vsm-VWAC3Y-7jr7ROL_xy0ufkIfqSke\", dest_path=\"D:/ratings.csv\")\n",
    "ratings = pd.read_csv(\"D:/ratings.csv\")\n",
    "ratings = ratings.drop([\"Unnamed: 0\"], axis=1)\n",
    "\n",
    "id2title = {row[\"id\"]:row[\"title\"] for _, row in movies.iterrows()}\n",
    "ratings[\"title\"] = ratings[\"movie_id\"].map(id2title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-16T04:43:09.535171Z",
     "start_time": "2022-01-16T04:43:09.166389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "density: 0.54%\n"
     ]
    }
   ],
   "source": [
    "n_users = ratings[\"user_id\"].nunique()\n",
    "n_items = ratings[\"movie_id\"].nunique()\n",
    "print(f\"density: {len(ratings)/(n_users*n_items):.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 평점을 준 사용자의 수가 매우 적은 영화 또는 평점을 매우 적게 남긴 사용자를 활용하면 분석에 왜곡이 발생할 수 있으므로 이를 방지하고 Data density를 높이기 위해 평점 갯수에 기준을 정하고 기준 이하인 샘플을 제거합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-16T04:43:18.297488Z",
     "start_time": "2022-01-16T04:43:17.933670Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_n_ratings = ratings[\"user_id\"].value_counts()\n",
    "cumsum_user = user_n_ratings.cumsum()/len(ratings)\n",
    "cumsum_user.reset_index(drop=True).plot.line();\n",
    "concat = pd.concat([user_n_ratings, cumsum_user], axis=1)\n",
    "concat.columns = [\"n_rating\", \"cumsum\"]\n",
    "# 평점을 `thr_user`개 이상 남긴 사용자가 전체 평점의 90%를 차지합니다.\n",
    "thr_user = concat[concat[\"cumsum\"]<0.9][\"n_rating\"].tolist()[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-16T04:43:19.840750Z",
     "start_time": "2022-01-16T04:43:19.060145Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "item_n_ratings = ratings[\"movie_id\"].value_counts()\n",
    "cumsum_item = item_n_ratings.cumsum()/len(ratings)\n",
    "item_n_ratings.cumsum().reset_index(drop=True).plot.line();\n",
    "concat = pd.concat([item_n_ratings, cumsum_item], axis=1)\n",
    "concat.columns = [\"n_rating\", \"cumsum\"]\n",
    "# 평점이 `thr_item`개 이상인 영화가 전체 평점의 90%를 차지합니다.\n",
    "thr_item = concat[concat[\"cumsum\"]<0.9][\"n_rating\"].tolist()[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-16T04:43:28.922634Z",
     "start_time": "2022-01-16T04:43:23.993275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Records: 80.4%\n"
     ]
    }
   ],
   "source": [
    "ratings_tar = ratings[(ratings[\"user_id\"].isin(user_n_ratings[user_n_ratings>=thr_user].index)) & (ratings[\"movie_id\"].isin(item_n_ratings[item_n_ratings>=thr_item].index))]\n",
    "print(f\"Number of Records: {len(ratings_tar)/len(ratings):.1%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User-Item Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T02:33:49.478468Z",
     "start_time": "2022-01-12T02:33:11.171838Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading...\n",
      "Completed!\n"
     ]
    }
   ],
   "source": [
    "file = \"D:/ui_movielens.csv\"\n",
    "gdd.download_file_from_google_drive(file_id=\"1jawc0X5l5HERfIirnEnhBatQ9lL3sefB\", dest_path=file)\n",
    "if os.path.exists(file):\n",
    "    print(\"Loading...\")\n",
    "    ui = pd.read_csv(file, index_col=0)\n",
    "else:\n",
    "    ui = pd.pivot_table(ratings_tar, index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n",
    "    print(\"Saving...\")\n",
    "    ui.to_csv(file)\n",
    "print(\"Completed!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T02:34:03.057887Z",
     "start_time": "2022-01-12T02:33:49.818175Z"
    }
   },
   "outputs": [],
   "source": [
    "avg_rating = ui.mean().mean()\n",
    "# User-Side Popularity-Opportunity Bias\n",
    "user_bias = ui.mean(axis=1) - avg_rating\n",
    "# Item-Side Popularity-Opportunity Bias\n",
    "item_bias = ui.mean(axis=0) - avg_rating\n",
    "adj_ui = ui.sub(user_bias, axis=0).sub(item_bias, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Similarity Measure\n",
    "\n",
    "### Consine Similarity\n",
    "\n",
    "#### Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T03:25:43.210345Z",
     "start_time": "2022-01-12T03:25:21.682665Z"
    }
   },
   "outputs": [],
   "source": [
    "adj_ui = adj_ui.fillna(0)\n",
    "# Vector Normalization\n",
    "norm_item = np.linalg.norm(adj_ui, axis=0, ord=2)\n",
    "adj_ui_norm = adj_ui.div(norm_item, axis=1)\n",
    "titles = [id2title[int(i)] for i in adj_ui.columns]\n",
    "cos_sim_item = pd.DataFrame(np.dot(adj_ui_norm.T, adj_ui_norm), index=titles, columns=titles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using `sklearn.metrics.pairwise.cosine_similarity()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T03:18:19.917309Z",
     "start_time": "2022-01-12T03:17:58.060341Z"
    }
   },
   "outputs": [],
   "source": [
    "# titles = [id2title[int(i)] for i in adj_ui.columns]\n",
    "cos_sim_item = pd.DataFrame(cosine_similarity(adj_ui.T), index=titles, columns=titles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Euclidean Similarity\n",
    "\n",
    "#### Implementation\n",
    "$$\n",
    "euc\\_dist(\\vec{X}, \\vec{Y}) = \\sqrt{\\sum(X_i - Y_i)^2} = \\sqrt{\\sum(X_i^2 + Y_i^2 - 2X_iY_i)} = \\sqrt{\\sum X_i^2 + \\sum Y_i^2 - 2\\vec{X} \\cdot \\vec{Y}}\n",
    "$$\n",
    "$$\n",
    "euc\\_sim(\\vec{X},\\vec{Y}) = \\frac{1}{1 + euc\\_dist(\\vec{X}, \\vec{Y})}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T05:08:15.345767Z",
     "start_time": "2022-01-12T05:07:48.149075Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-123-69c40a32158e>:4: RuntimeWarning: invalid value encountered in sqrt\n",
      "  euc_dist_item = np.sqrt(square - 2*dot)\n"
     ]
    }
   ],
   "source": [
    "adj_ui = adj_ui.fillna(0)\n",
    "square = np.array(np.square(adj_ui).sum(axis=0))\n",
    "square = np.add.outer(square, square)\n",
    "dot = np.dot(adj_ui.T, adj_ui)\n",
    "euc_dist_item = np.sqrt(square - 2*dot)\n",
    "# titles = [id2title[int(i)] for i in adj_ui.columns]\n",
    "euc_sim_item = 1/(1 + euc_dist_item)\n",
    "np.fill_diagonal(euc_sim_item, 1)\n",
    "euc_sim_item = pd.DataFrame(euc_sim_item, index=titles, columns=titles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using `sklearn.metrics.pairwise.euclidean_distances()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T05:06:26.332948Z",
     "start_time": "2022-01-12T05:06:13.651593Z"
    }
   },
   "outputs": [],
   "source": [
    "euc_dist_item = euclidean_distances(adj_ui.T)\n",
    "euc_sim_item = pd.DataFrame(1/(1 + euc_dist_item), index=titles, columns=titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T05:06:26.966405Z",
     "start_time": "2022-01-12T05:06:26.936070Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Toy Story</th>\n",
       "      <th>Jumanji</th>\n",
       "      <th>Grumpier Old Men</th>\n",
       "      <th>Waiting to Exhale</th>\n",
       "      <th>Father of the Bride Part II</th>\n",
       "      <th>Heat</th>\n",
       "      <th>Sabrina</th>\n",
       "      <th>Tom and Huck</th>\n",
       "      <th>Sudden Death</th>\n",
       "      <th>GoldenEye</th>\n",
       "      <th>...</th>\n",
       "      <th>Her</th>\n",
       "      <th>The Lego Movie</th>\n",
       "      <th>Grand Budapest Hotel, The</th>\n",
       "      <th>Interstellar</th>\n",
       "      <th>Captain America: The Winter Soldier</th>\n",
       "      <th>X-Men: Days of Future Past</th>\n",
       "      <th>Edge of Tomorrow</th>\n",
       "      <th>Boyhood</th>\n",
       "      <th>Gone Girl</th>\n",
       "      <th>Guardians of the Galaxy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Toy Story</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001697</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.001561</td>\n",
       "      <td>0.001609</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.001609</td>\n",
       "      <td>0.001566</td>\n",
       "      <td>0.001571</td>\n",
       "      <td>0.001650</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001563</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.001562</td>\n",
       "      <td>0.001560</td>\n",
       "      <td>0.001565</td>\n",
       "      <td>0.001565</td>\n",
       "      <td>0.001564</td>\n",
       "      <td>0.001560</td>\n",
       "      <td>0.001560</td>\n",
       "      <td>0.001566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumanji</th>\n",
       "      <td>0.001697</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002068</td>\n",
       "      <td>0.002109</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001849</td>\n",
       "      <td>0.002040</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.002038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002091</td>\n",
       "      <td>0.002105</td>\n",
       "      <td>0.002081</td>\n",
       "      <td>0.002091</td>\n",
       "      <td>0.002103</td>\n",
       "      <td>0.002102</td>\n",
       "      <td>0.002095</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.002096</td>\n",
       "      <td>0.002094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grumpier Old Men</th>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.002068</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003033</td>\n",
       "      <td>0.003016</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>0.002791</td>\n",
       "      <td>0.003108</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.001965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002935</td>\n",
       "      <td>0.003009</td>\n",
       "      <td>0.002908</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>0.003001</td>\n",
       "      <td>0.002980</td>\n",
       "      <td>0.002964</td>\n",
       "      <td>0.003038</td>\n",
       "      <td>0.002988</td>\n",
       "      <td>0.002935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waiting to Exhale</th>\n",
       "      <td>0.001561</td>\n",
       "      <td>0.002109</td>\n",
       "      <td>0.003033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003159</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.003010</td>\n",
       "      <td>0.005094</td>\n",
       "      <td>0.004239</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004639</td>\n",
       "      <td>0.004923</td>\n",
       "      <td>0.004512</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.004910</td>\n",
       "      <td>0.004808</td>\n",
       "      <td>0.004756</td>\n",
       "      <td>0.005165</td>\n",
       "      <td>0.004880</td>\n",
       "      <td>0.004602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Father of the Bride Part II</th>\n",
       "      <td>0.001609</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.003016</td>\n",
       "      <td>0.003159</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002047</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.003155</td>\n",
       "      <td>0.001976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003018</td>\n",
       "      <td>0.003094</td>\n",
       "      <td>0.002984</td>\n",
       "      <td>0.003042</td>\n",
       "      <td>0.003087</td>\n",
       "      <td>0.003062</td>\n",
       "      <td>0.003047</td>\n",
       "      <td>0.003127</td>\n",
       "      <td>0.003073</td>\n",
       "      <td>0.003015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3413 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Toy Story   Jumanji  Grumpier Old Men  \\\n",
       "Toy Story                     1.000000  0.001697          0.001610   \n",
       "Jumanji                       0.001697  1.000000          0.002068   \n",
       "Grumpier Old Men              0.001610  0.002068          1.000000   \n",
       "Waiting to Exhale             0.001561  0.002109          0.003033   \n",
       "Father of the Bride Part II   0.001609  0.002116          0.003016   \n",
       "\n",
       "                             Waiting to Exhale  Father of the Bride Part II  \\\n",
       "Toy Story                             0.001561                     0.001609   \n",
       "Jumanji                               0.002109                     0.002116   \n",
       "Grumpier Old Men                      0.003033                     0.003016   \n",
       "Waiting to Exhale                     1.000000                     0.003159   \n",
       "Father of the Bride Part II           0.003159                     1.000000   \n",
       "\n",
       "                                 Heat   Sabrina  Tom and Huck  Sudden Death  \\\n",
       "Toy Story                    0.001610  0.001609      0.001566      0.001571   \n",
       "Jumanji                      0.001849  0.002040      0.002151      0.002100   \n",
       "Grumpier Old Men             0.002075  0.002791      0.003108      0.003099   \n",
       "Waiting to Exhale            0.002119  0.003010      0.005094      0.004239   \n",
       "Father of the Bride Part II  0.002047  0.002887      0.003229      0.003155   \n",
       "\n",
       "                             GoldenEye  ...       Her  The Lego Movie  \\\n",
       "Toy Story                     0.001650  ...  0.001563        0.001569   \n",
       "Jumanji                       0.002038  ...  0.002091        0.002105   \n",
       "Grumpier Old Men              0.001965  ...  0.002935        0.003009   \n",
       "Waiting to Exhale             0.001995  ...  0.004639        0.004923   \n",
       "Father of the Bride Part II   0.001976  ...  0.003018        0.003094   \n",
       "\n",
       "                             Grand Budapest Hotel, The  Interstellar  \\\n",
       "Toy Story                                     0.001562      0.001560   \n",
       "Jumanji                                       0.002081      0.002091   \n",
       "Grumpier Old Men                              0.002908      0.002956   \n",
       "Waiting to Exhale                             0.004512      0.004757   \n",
       "Father of the Bride Part II                   0.002984      0.003042   \n",
       "\n",
       "                             Captain America: The Winter Soldier  \\\n",
       "Toy Story                                               0.001565   \n",
       "Jumanji                                                 0.002103   \n",
       "Grumpier Old Men                                        0.003001   \n",
       "Waiting to Exhale                                       0.004910   \n",
       "Father of the Bride Part II                             0.003087   \n",
       "\n",
       "                             X-Men: Days of Future Past  Edge of Tomorrow  \\\n",
       "Toy Story                                      0.001565          0.001564   \n",
       "Jumanji                                        0.002102          0.002095   \n",
       "Grumpier Old Men                               0.002980          0.002964   \n",
       "Waiting to Exhale                              0.004808          0.004756   \n",
       "Father of the Bride Part II                    0.003062          0.003047   \n",
       "\n",
       "                              Boyhood  Gone Girl  Guardians of the Galaxy  \n",
       "Toy Story                    0.001560   0.001560                 0.001566  \n",
       "Jumanji                      0.002100   0.002096                 0.002094  \n",
       "Grumpier Old Men             0.003038   0.002988                 0.002935  \n",
       "Waiting to Exhale            0.005165   0.004880                 0.004602  \n",
       "Father of the Bride Part II  0.003127   0.003073                 0.003015  \n",
       "\n",
       "[5 rows x 3413 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euc_sim_item.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-12T05:13:03.448725Z",
     "start_time": "2022-01-12T05:13:03.413471Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Forrest Gump                          0.630181\n",
       "Star Wars: Episode IV - A New Hope    0.628852\n",
       "Jurassic Park                         0.626524\n",
       "Toy Story 2                           0.624472\n",
       "Back to the Future                    0.623630\n",
       "Name: Toy Story, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Toy Story 2       0.001974\n",
       "Aladdin           0.001862\n",
       "Lion King, The    0.001844\n",
       "Bug's Life, A     0.001834\n",
       "Monsters, Inc.    0.001813\n",
       "Name: Toy Story, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Toy Story\"\n",
    "display(cos_sim_item[title].sort_values(ascending=False)[1:6])\n",
    "display(euc_sim_item[title].sort_values(ascending=False)[1:6])"
   ]
  }
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