GeneralML/RecommendationModel/Recommendation System - Matrix Factoization (Collaboratvie Filtering) using Surprise Library.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommendation System - Matrix Factoization (Collaboratvie Filtering) using Surprise Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data is [MovieLens](https://grouplens.org/datasets/movielens/) data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "from scipy.sparse.linalg import svds\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import os\n",
    "\n",
    "from fastai import *\n",
    "from fastai.collab import *\n",
    "from fastai.tabular import *\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from collections import defaultdict #data colector\n",
    "\n",
    "#Surprise: https://surprise.readthedocs.io/en/stable/\n",
    "import surprise\n",
    "\n",
    "from surprise.reader import Reader\n",
    "from surprise import Dataset\n",
    "from surprise.model_selection import GridSearchCV\n",
    "from surprise import NormalPredictor\n",
    "from surprise import accuracy\n",
    "from surprise import KNNBasic\n",
    "\n",
    "  ##CrossValidation\n",
    "from surprise.model_selection import cross_validate\n",
    "\n",
    "##Matrix Factorization Algorithms\n",
    "from surprise import SVD\n",
    "from surprise import NMF\n",
    "\n",
    "np.random.seed(623) # replicating results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Opening ZIP file  \n",
    "# from io import BytesIO\n",
    "# from zipfile import ZipFile\n",
    "# from urllib.request import urlopen\n",
    "\n",
    "# r = urlopen(\"http://files.grouplens.org/datasets/movielens/ml-latest-small.zip\")\n",
    "# zipfile = ZipFile(BytesIO(r.read()))\n",
    "\n",
    "# rating_data = pd.read_csv(zipfile.open('ml-latest-small/ratings.csv'))\n",
    "# movie_data = pd.read_csv(zipfile.open('ml-latest-small/movies.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pth = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_data  = pd.read_csv(os.path.join(pth,'ratings.csv'))\n",
    "movie_data  = pd.read_csv(os.path.join(pth,'movies.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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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",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
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      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. PreProcessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_ratings = 2 \n",
    "min_ratings_count =  5 \n",
    "# Delete movie rated under 2 \n",
    "# Delete movie rated less than 5 times\n",
    "ratings_flrd_df = rating_data.groupby(\"movieId\").filter(lambda x: x['movieId'].count() >= min_ratings)\n",
    "ratings_flrd_df = ratings_flrd_df.groupby(\"userId\").filter(lambda x: x['userId'].count() >= min_ratings_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97390, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_flrd_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When upload your own rating dataset either from a file (e.g. csv), a seprate reader should be defined\n",
    "Reference is [here](https://surprise.readthedocs.io/en/stable/getting_started.html#use-a-custom-dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = Reader(rating_scale=(0.5, 5)) #line_format by default order of the fields\n",
    "\n",
    "# Surprise Dataset Load method\n",
    "data = Dataset.load_from_df(ratings_flrd_df[[\"userId\",\"movieId\",\"rating\"]], reader=reader)\n",
    "\n",
    "trainset = data.build_full_trainset()\n",
    "\n",
    "testset = trainset.build_anti_testset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.trainset.Trainset at 0x1f18713ab88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matrix Factorization\n",
    "\n",
    "Hopcroft and Kannan (2012), explains the whole concept of matrix factorization on customer data where m customers buy n products. The authors explain collaborative filtering in a comprehensive language. For demonstrative purposes, the author of this article demonstrates the concept on a specific case.\n",
    "\n",
    "Let matrix $R_{m*n}$ represent the ratings on movies assigned by each user, also called the utility matrix. Specifically, the value $r_{ij} = 5$ represents the rating of user i assigned to movie j. However, the individual's preference is determined by k factors. For example, the user's age, sex, income, education, etc. are likely to affect the user's behavior. Accordingly, the individual's rating of a movie ($r_{ij}$) is determined by some weighted combinations of the hidden factors. In practice, customer's behavior can be characterized by a k-dimensional vector with much lower dimensions than the original matrix $R$ with $m * n$ dimensions. The vector's components, also called the latent factors, represent the weight of each factor. For example, given a vector $v_2 = [0.2 , 0.8]$ it can be hypothesized that there are only two (unknown) latent factors with subsequent weights describing the rating (behavior).\n",
    "\n",
    "Matrix factorization is an effective CF technique because it benefits from the properties of linear algebra. Specifically, consider matrix $R$ as a record of various elements. As it is possible to decompose any integer into the product of its prime factor, matrix factorization also enables humans to explore information about matrices and their functional properties an array of elements (Goodfellow, Bengio, 2016)\n",
    "\n",
    "Singular Value Decomposition (SVD)\n",
    "\n",
    "SVD decomposes any matrix into singular vectors and singular values. If the reader has previous experience with machine learning, particularly with dimensionality reduction, they would find traditional use of SVD in Principal Component Analysis (PCA). Simply put, SVD is equivalent to PCA after mean centering, i.e. shifting all data points so that their mean is on the origin (Gillis, 2014).\n",
    "\n",
    "Formally, SVD is decomposition of a matrix R into the product of three matrices: $R_{m*n} = U_{m*m} D_{m*n} V_{n*n}^{t}$.\n",
    "\n",
    "Where $R_{m*n}$ denotes the utility matrix with n equal to the number of e.g. users and m number exposed items (movies). $U_{m*m}$ is a left singular orthogonal matrix, representing the relationship between users and latent factors (Hopcroft & Kannan, 2012). $D_{m*n}$ is a diagonal matrix (with positive real values) describing the strength of each latent factor. $V_{n*n}^{t}$ (transpose) is a right singular orthogonal matrix, indicating the similarity between items and latent factors.\n",
    "\n",
    "The general goal of SVD (and other matrix factorization methods) is to decompose the matrix R with all missing $r_{ij}$ and multiply its components $U_{m*m} D_{m*n} V_{n*n}^{t}$ once again. As a result, there are no missing values $r_{ij}$ and it is possible to recommend each user movies (items) they have not seen or purchased yet. To better understand linear algebra behind SVD, one can watch Gilbert Strang's lecture on SVD for MIT OpenCourseWare on YouTube channel or visit refer to NIT Singular Value Decomposition (SVD) tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference: \n",
    "(1) https://www.cs.cornell.edu/jeh/book.pdf\n",
    "(2) https://analyticsindiamag.com/singular-value-decomposition-svd-application-recommender-system/#:~:text=In%20the%20context%20of%20the,given%20to%20items%20by%20users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Surprise Library has a built-in SVD Computation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The following function computes the Cross validated RMSE. And, the return contains 100 measures of min RMSE given 100 consecutive values of k in each test set, and by five folds in every iteration (from [surprise document](http://surpriselib.com/))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just checking the effect of number of latent factors k on the model's performance.\n",
    "def rmse_vs_factors(algorithm, data):\n",
    "    \"\"\"Returns: rmse_algorithm i.e. a list of mean RMSE of CV = 5 in cross_validate() for each  factor k in range(1, 101, 1) 100 values \n",
    "    Arg:  i.) algorithm = Matrix factoization algorithm, e.g SVD/NMF/PMF, ii.)  data = surprise.dataset.DatasetAutoFolds\n",
    "    \"\"\"\n",
    "    rmse_algorithm = []\n",
    "    \n",
    "    for k in range(1, 101, 1):\n",
    "        algo = algorithm(n_factors = k)\n",
    "\n",
    "        #[\"test_rmse\"] is a numpy array with min accuracy value for each testset\n",
    "        loss_fce = cross_validate(algo, data, measures=['RMSE'], cv=5, verbose=False)[\"test_rmse\"].mean() \n",
    "        rmse_algorithm.append(loss_fce)\n",
    "  \n",
    "    return rmse_algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_svd = rmse_vs_factors(SVD,data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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     "execution_count": 17,
     "metadata": {},
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   "source": [
    "rmse_svd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. function plot_rmse() with two arguments where rmse is a list of float values and algorithm is an instantiated matrix factorization model. \n",
    "2. The function returns a plot with two line subplots that display performance vs. numbers of factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_rmse(rmse, algorithm):\n",
    "    plt.figure(num=None, figsize=(11, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "        \n",
    "    plt.subplot(2,1,1)\n",
    "    plt.plot(rmse)\n",
    "    plt.xlim(0,100)\n",
    "    plt.title(\"{0} Performance: RMSE Against Number of Factors\".format(algorithm), size = 20 )\n",
    "    plt.ylabel(\"Mean RMSE (cv=5)\")\n",
    "\n",
    "    plt.subplot(2,1,2)\n",
    "    plt.plot(rmse)\n",
    "    plt.xlim(0,50)\n",
    "    plt.xticks(np.arange(0, 52, step=2))\n",
    "\n",
    "    plt.xlabel(\"{0}(n_factor = k)\".format(algorithm))\n",
    "    plt.ylabel(\"Mean RMSE (cv=5)\")\n",
    "    plt.axvline(np.argmin(rmse), color = \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 880x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_rmse(rmse_svd,\"SVD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the figure, there is an increasing trend of worse performance with higher k. The lowest RMSE is achieved when $k=4$. However, it is worth mentioning that $k=14$ is also very close to the RMSE achieved with only 4 latent factors. Besides, the author argues that it is not probable that the user's taste (rating) is determined by such a low number of factors. On the other hand, the result suggests a range of values which can be used in GridSearchCV()for parameter tunning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8633595626314179\n",
      "{'n_factors': 18}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'n_factors': [4,6,9,11,14,18,29]}\n",
    "gs = GridSearchCV(SVD, param_grid, measures=['rmse'], cv=5)\n",
    "gs.fit(data)\n",
    "\n",
    "\n",
    "# best RMSE score\n",
    "print(gs.best_score['rmse'])\n",
    "\n",
    "# combination of parameters that gave the best RMSE score\n",
    "print(gs.best_params['rmse'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To make the model generalizable, i.e. avoid over and underfitting, the grid algorithm finds $n_factors = 18$ optimal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Then, Training SVD Algorithm and Predictions\n",
    "#### To predict ratings, for each empty element $a_{ij}$ in the utility matrix, it is essential to specify: a.) the users and b.) particular movies that are not in the trainset. So, build_anti_testset() method of trainset accomplishes the goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Prediction(uid=1, iid=318, r_ui=3.5110432282575212, est=5, details={'was_impossible': False}),\n",
       " Prediction(uid=1, iid=1704, r_ui=3.5110432282575212, est=4.750784530374444, details={'was_impossible': False})]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo_SVD = SVD(n_factors = 18)\n",
    "algo_SVD.fit(trainset)\n",
    "\n",
    "\n",
    "# Predict ratings for all pairs (i,j) that are NOT in the training set.\n",
    "testset = trainset.build_anti_testset()\n",
    "\n",
    "predictions = algo_SVD.test(testset)\n",
    "\n",
    "# subset of the list  predictions\n",
    "predictions[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_n(predictions, userId, movies_df, ratings_df, n = 10):\n",
    "    '''Return the top N (default) movieId for a user,.i.e. userID and history for comparisom\n",
    "    Args:\n",
    "    Returns: \n",
    "  \n",
    "    '''\n",
    "    #Peart I.: Surprise docomuntation\n",
    "    \n",
    "    #1. First map the predictions to each user.\n",
    "    top_n = defaultdict(list)\n",
    "    for uid, iid, true_r, est, _ in predictions:\n",
    "        top_n[uid].append((iid, est))\n",
    "\n",
    "    #2. Then sort the predictions for each user and retrieve the k highest ones.\n",
    "    for uid, user_ratings in top_n.items():\n",
    "        user_ratings.sort(key = lambda x: x[1], reverse = True)\n",
    "        top_n[uid] = user_ratings[: n ]\n",
    "    \n",
    "    #Part II.: inspired by: https://beckernick.github.io/matrix-factorization-recommender/\n",
    "    \n",
    "    #3. Tells how many movies the user has already rated\n",
    "    user_data = ratings_df[ratings_df.userId == (userId)]\n",
    "    print('User {0} has already rated {1} movies.'.format(userId, user_data.shape[0]))\n",
    "\n",
    "    \n",
    "    #4. Data Frame with predictions. \n",
    "    preds_df = pd.DataFrame([(id, pair[0],pair[1]) for id, row in top_n.items() for pair in row],\n",
    "                        columns=[\"userId\" ,\"movieId\",\"rat_pred\"])\n",
    "    \n",
    "    \n",
    "    #5. Return pred_usr, i.e. top N recommended movies with (merged) titles and genres. \n",
    "    pred_usr = preds_df[preds_df[\"userId\"] == (userId)].merge(movies_df, how = 'left', left_on = 'movieId', right_on = 'movieId')\n",
    "            \n",
    "    #6. Return hist_usr, i.e. top N historically rated movies with (merged) titles and genres for holistic evaluation\n",
    "    hist_usr = ratings_df[ratings_df.userId == (userId) ].sort_values(\"rating\", ascending = False).merge\\\n",
    "    (movies_df, how = 'left', left_on = 'movieId', right_on = 'movieId')\n",
    "    \n",
    "    \n",
    "    return hist_usr, pred_usr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SVD Recommendations\n",
    "\n",
    "Since the model was properly trained, it is already possible to suggest any userID n movies at this stage. Additionally, based on the acquired predictions and defined get_top_n() function it is reasonable to visually inspect the recommended movies and the user's highest rated movies in the past. For instance, let's assume the userId 124 wants to watch a movie at their earliest convenience but has no specific title in mind. On top of that, the movie database contains over 6,000 titles across multiple genres so the user would spend a lot of time researching for what movies are in line with respect to their specific preferences. The following code makes the user's choice much easier. After calling get_top_n() function, it is immediately obvious the user 124 has already rated 50 movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User 124 has already rated 50 movies.\n"
     ]
    }
   ],
   "source": [
    "hist_SVD_124, pred_SVD_124 = get_top_n(predictions, movies_df = movie_data, userId = 124, ratings_df = rating_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>124</td>\n",
       "      <td>1358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1336409590</td>\n",
       "      <td>Sling Blade (1996)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>124</td>\n",
       "      <td>3949</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1336413175</td>\n",
       "      <td>Requiem for a Dream (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124</td>\n",
       "      <td>7361</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1336413015</td>\n",
       "      <td>Eternal Sunshine of the Spotless Mind (2004)</td>\n",
       "      <td>Drama|Romance|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>124</td>\n",
       "      <td>6377</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1336584435</td>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>124</td>\n",
       "      <td>2858</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1336584459</td>\n",
       "      <td>American Beauty (1999)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>124</td>\n",
       "      <td>356</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1336423402</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>Comedy|Drama|Romance|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>124</td>\n",
       "      <td>608</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1336412883</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>124</td>\n",
       "      <td>3252</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1336409712</td>\n",
       "      <td>Scent of a Woman (1992)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>124</td>\n",
       "      <td>1210</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1336584462</td>\n",
       "      <td>Star Wars: Episode VI - Return of the Jedi (1983)</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>124</td>\n",
       "      <td>1196</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1336584447</td>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back...</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>124</td>\n",
       "      <td>3328</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1336413358</td>\n",
       "      <td>Ghost Dog: The Way of the Samurai (1999)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>124</td>\n",
       "      <td>1884</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1336584611</td>\n",
       "      <td>Fear and Loathing in Las Vegas (1998)</td>\n",
       "      <td>Adventure|Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>124</td>\n",
       "      <td>4226</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1336413304</td>\n",
       "      <td>Memento (2000)</td>\n",
       "      <td>Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>124</td>\n",
       "      <td>3147</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1336412867</td>\n",
       "      <td>Green Mile, The (1999)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>124</td>\n",
       "      <td>5608</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1336413269</td>\n",
       "      <td>Das Experiment (Experiment, The) (2001)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId  movieId  rating   timestamp  \\\n",
       "0      124     1358     5.0  1336409590   \n",
       "1      124     3949     5.0  1336413175   \n",
       "2      124     7361     5.0  1336413015   \n",
       "3      124     6377     5.0  1336584435   \n",
       "4      124     2858     5.0  1336584459   \n",
       "5      124      356     5.0  1336423402   \n",
       "6      124      608     5.0  1336412883   \n",
       "7      124     3252     4.5  1336409712   \n",
       "8      124     1210     4.5  1336584462   \n",
       "9      124     1196     4.5  1336584447   \n",
       "10     124     3328     4.5  1336413358   \n",
       "11     124     1884     4.5  1336584611   \n",
       "12     124     4226     4.5  1336413304   \n",
       "13     124     3147     4.5  1336412867   \n",
       "14     124     5608     4.5  1336413269   \n",
       "\n",
       "                                                title  \\\n",
       "0                                  Sling Blade (1996)   \n",
       "1                          Requiem for a Dream (2000)   \n",
       "2        Eternal Sunshine of the Spotless Mind (2004)   \n",
       "3                                 Finding Nemo (2003)   \n",
       "4                              American Beauty (1999)   \n",
       "5                                 Forrest Gump (1994)   \n",
       "6                                        Fargo (1996)   \n",
       "7                             Scent of a Woman (1992)   \n",
       "8   Star Wars: Episode VI - Return of the Jedi (1983)   \n",
       "9   Star Wars: Episode V - The Empire Strikes Back...   \n",
       "10           Ghost Dog: The Way of the Samurai (1999)   \n",
       "11              Fear and Loathing in Las Vegas (1998)   \n",
       "12                                     Memento (2000)   \n",
       "13                             Green Mile, The (1999)   \n",
       "14            Das Experiment (Experiment, The) (2001)   \n",
       "\n",
       "                                 genres  \n",
       "0                                 Drama  \n",
       "1                                 Drama  \n",
       "2                  Drama|Romance|Sci-Fi  \n",
       "3   Adventure|Animation|Children|Comedy  \n",
       "4                         Drama|Romance  \n",
       "5              Comedy|Drama|Romance|War  \n",
       "6           Comedy|Crime|Drama|Thriller  \n",
       "7                                 Drama  \n",
       "8               Action|Adventure|Sci-Fi  \n",
       "9               Action|Adventure|Sci-Fi  \n",
       "10                          Crime|Drama  \n",
       "11               Adventure|Comedy|Drama  \n",
       "12                     Mystery|Thriller  \n",
       "13                          Crime|Drama  \n",
       "14                       Drama|Thriller  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see the user's history, let's examine their top 15 highest rated movies\n",
    "hist_SVD_124.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rat_pred</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>124</td>\n",
       "      <td>750</td>\n",
       "      <td>4.600220</td>\n",
       "      <td>Dr. Strangelove or: How I Learned to Stop Worr...</td>\n",
       "      <td>Comedy|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>124</td>\n",
       "      <td>1204</td>\n",
       "      <td>4.577019</td>\n",
       "      <td>Lawrence of Arabia (1962)</td>\n",
       "      <td>Adventure|Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124</td>\n",
       "      <td>904</td>\n",
       "      <td>4.561244</td>\n",
       "      <td>Rear Window (1954)</td>\n",
       "      <td>Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>124</td>\n",
       "      <td>2959</td>\n",
       "      <td>4.529422</td>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>124</td>\n",
       "      <td>1104</td>\n",
       "      <td>4.519563</td>\n",
       "      <td>Streetcar Named Desire, A (1951)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>124</td>\n",
       "      <td>912</td>\n",
       "      <td>4.513226</td>\n",
       "      <td>Casablanca (1942)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>124</td>\n",
       "      <td>2571</td>\n",
       "      <td>4.501588</td>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "      <td>Action|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>124</td>\n",
       "      <td>4973</td>\n",
       "      <td>4.494541</td>\n",
       "      <td>Amelie (Fabuleux destin d'Amélie Poulain, Le) ...</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>124</td>\n",
       "      <td>2324</td>\n",
       "      <td>4.493342</td>\n",
       "      <td>Life Is Beautiful (La Vita è bella) (1997)</td>\n",
       "      <td>Comedy|Drama|Romance|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>124</td>\n",
       "      <td>898</td>\n",
       "      <td>4.493223</td>\n",
       "      <td>Philadelphia Story, The (1940)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rat_pred  \\\n",
       "0     124      750  4.600220   \n",
       "1     124     1204  4.577019   \n",
       "2     124      904  4.561244   \n",
       "3     124     2959  4.529422   \n",
       "4     124     1104  4.519563   \n",
       "5     124      912  4.513226   \n",
       "6     124     2571  4.501588   \n",
       "7     124     4973  4.494541   \n",
       "8     124     2324  4.493342   \n",
       "9     124      898  4.493223   \n",
       "\n",
       "                                               title  \\\n",
       "0  Dr. Strangelove or: How I Learned to Stop Worr...   \n",
       "1                          Lawrence of Arabia (1962)   \n",
       "2                                 Rear Window (1954)   \n",
       "3                                  Fight Club (1999)   \n",
       "4                   Streetcar Named Desire, A (1951)   \n",
       "5                                  Casablanca (1942)   \n",
       "6                                 Matrix, The (1999)   \n",
       "7  Amelie (Fabuleux destin d'Amélie Poulain, Le) ...   \n",
       "8         Life Is Beautiful (La Vita è bella) (1997)   \n",
       "9                     Philadelphia Story, The (1940)   \n",
       "\n",
       "                        genres  \n",
       "0                   Comedy|War  \n",
       "1          Adventure|Drama|War  \n",
       "2             Mystery|Thriller  \n",
       "3  Action|Crime|Drama|Thriller  \n",
       "4                        Drama  \n",
       "5                Drama|Romance  \n",
       "6       Action|Sci-Fi|Thriller  \n",
       "7               Comedy|Romance  \n",
       "8     Comedy|Drama|Romance|War  \n",
       "9         Comedy|Drama|Romance  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction\n",
    "pred_SVD_124"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TODO: Try another algorithm (other than SVD) to see if there are any characteristics that you could find in prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Refernece: \n",
    "##### (1) https://towardsdatascience.com/creating-a-hybrid-content-collaborative-movie-recommender-using-deep-learning-cc8b431618af\n",
    "##### (2) Sarwar, Badrul & Badrul, & Karypis, George & Cybenko, George & Konstan, & Joseph, & Reidl, & Tsibouklis, John. (2001). Item-based collaborative filtering recommendation algorithmus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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