recommender_system_report.ipynb

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    "# Restaurant Recommender System\n",
    "\n",
    "**Authors**: Lyla Kiratiwudhikul, Mina Lee, Tom Zhang\n",
    "\n",
    "**Table of Contents**:\n",
    "\n",
    "1. Motivations and Objectives\n",
    "2. Data Overview and Preprocessing\n",
    "3. Exploratory Data Analysis\n",
    "4. Modeling\n",
    "    - Baseline Model\n",
    "    - Neural Collaborative Filtering (NCF) Model"
   ]
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  {
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   "source": [
    "from pprint import pprint\n",
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from surprise import Dataset, Reader, KNNWithMeans  # pip install scikit-surprise\n",
    "from surprise.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Embedding, Flatten, Dot, Dense, Concatenate\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "from textblob import TextBlob  # pip install textblob\n",
    "\n",
    "np.random.seed(209)\n",
    "t0 = datetime.now()"
   ]
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    "## Motivations and Objectives\n",
    "\n",
    "For the past few decades, with the fast growing market of digital platforms, \n",
    "companies have tried to customize the advertising of their products based on individual customers' preferences or interests. \n",
    "This practice has been utilized across various industries and companies, from the e-commerce site Amazon suggesting relevant products to the streaming platform Netflix recommending similar shows to their users' view history and profile. The recommendation systems help increase sales as the users are able to easily see and purchase recommended products that match their needs and preferences.\n",
    "\n",
    "In this project, we focus on devising a restaurant recommendation system (hereby referred to as “recommender”).\n",
    "We use data of restaurants and customer profiles from Yelp, a platform for crowd-sourced reviews about businesses.\n",
    "As an individual has unique restaurant preferences, such as cuisines, ambience, pets, diet types, and/or parking availability, we aim to build the recommender to recommend restaurants to users based on the insights gleaned from their reviews on the previous restaurants they have been to."
   ]
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    "## Data Overview\n",
    "\n",
    "The data is downloaded from [Yelp official website](https://www.yelp.com/dataset/documentation/main). There are three datasets relevant to our analysis and models: `business`, `review`, and `user` data. The `business` dataset contains information about the businesses including name, location, hours, average rating stars, hours, number of reviews, and other features such as cuisine types and parking availability. The `user` data include the user's friend mapping and all the metadata associated with the user such as number of upvotes. The `review` dataset records full review text data as well as the `user_id` who wrote the review and the `business_id` for which the review was written. There are 150,346 businesses and 6,990,280 reviews in the Yelp original datasets. Below is the list of features in the two raw datasets:\n",
    "\n",
    "**Business:**\n",
    "- `business_id`: business’s ID, string\n",
    "- `name`: business’s name, string\n",
    "- `address`: business’s full address, string\n",
    "- `city`: business’s city, string\n",
    "- `state`: business’s state, string, 2 character state code, if applicable\n",
    "- `postal_code`: business’s postal code, string\n",
    "- `latitude`: business’s latitude, float\n",
    "- `longitude`: business’s longitude, float\n",
    "- `stars`: business’s average stars rating, float (ranging from 1 to 5, with 0.5 increment)\n",
    "- `review_count`: business’s number of reviews, integer\n",
    "- `is_open`\t: whether the business is open or closed; 0 or 1 for closed or open, integer\n",
    "- `attributes`: business’s features (e.g whether it offers parking, whether it accept credit cards, etc), JSON object\n",
    "- `categories`: business’s categories (e.g. “Mexican”, “Burger”, etc), array of strings\n",
    "- `hours`: business’s working hours, object of key day (Monday-Sunday) to value hours\n",
    "\n",
    "**Review:**\n",
    "- `review_id`: unique review ID, string\n",
    "- `user_id`: 22 character user’s ID, string\n",
    "- `business_id`: 22 character business’s ID, string\n",
    "- `stars`: business’s stars rating, integer (1 to 5)\n",
    "- `useful`: number of useful votes received, integer\n",
    "- `integer`: number of funny votes received, integer\n",
    "- `cool`: number of cool votes received, integer\t\n",
    "- `text`: review itself, string\n",
    "- `date`: date format YYYY-MM-DD, string\n",
    "\n",
    "**User:**\n",
    "- `user_id`: 22 character user’s ID, string\n",
    "- `name`: user's first name, string \n",
    "- `review_count`: number of reviews they've written, int  \n",
    "- `yelping_since`: date the user joined Yelp, string in format YYYY-MM-DD\n",
    "- `useful`: number of useful votes sent by the user, int \n",
    "- `funny`: number of funny votes sent by the user, int \n",
    "- `cool`: number of cool votes sent by the user, int \n",
    "- `elite`: the years the user was elite, array of integers, \n",
    "- `friends`: an array of the user's friend as user_ids, array of strings \n",
    "- `fans`: number of fans the user has, int \n",
    "- `average_stars`: average rating of all reviews, float\n",
    "- `compliment_hot`: number of hot compliments received by the user, int\n",
    "- `compliment_more`: number of more compliments received by the user, int  \n",
    "- `compliment_profile`: number of profile compliments received by the user, int \n",
    "- `compliment_cute`: number of cute compliments received by the user, int\n",
    "- `compliment_list`: number of list compliments received by the user, int\n",
    "- `compliment_note`: number of note compliments received by the user, int\n",
    "- `compliment_plain`: number of plain compliments received by the user, int\n",
    "- `compliment_cool`: number of cool compliments received by the user, int\n",
    "- `compliment_funny`: number of funny compliments received by the user, int \n",
    "- `compliment_writer`: number of writer compliments received by the user, int\n",
    "- `compliment_photos`: number of photo compliments received by the user, int"
   ]
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    "### Data Preparation\n",
    "\n",
    "**Business:**\n",
    "\n",
    "As the main goal of this project is to build a recommender system that suggests top restaurants and their offerings based on user location, we have excluded the entries that are not classified as restaurants such as “spas”, “hotels”, and “hair salons” by dropping all rows whose `categories` do not contain any of the following keywords: “food”, “restaurant”, “bar”, “pubs”, “restaurant”, “tea”, and “coffee”. This has decreased the number of entries from 150,346 to 69,253.\n",
    "\n",
    "Further, we have expanded `attributes` columns based on the JSON objects given. The first attempt returned 39 new columns. However, some of the expanded features contained nested JSON objects, so we further expanded `BusinessParking`, `Ambience`, `DietaryRestrictons`, and `Music` features and concatenated them to the other attributes, resulting in final 62 columns.\n",
    "\n",
    "In addition, the missing values in original and newly created attributes based features are filled with `None`, indicating that the information is not available. We decided to impute the missing values this way instead of mode imputation method because there are users who might be indifferent regarding the missing attributes and those who are concerned about them (e.g. whether there is parking or not).\n",
    "\n",
    "For `hours`, an expansion of operating hour objects into seven days was performed first. For the 9,710 missing entries for hours, we imputed the operating hours with the modes of the dataset for businesses whose value for the hours column was completely missing and imputed with `\"closed\"` for businesses whose hours are available for only some days and are not provided for the other days (i.e. we assumed that these restaurants are closed on the missing days).\n",
    "\n",
    "Furthermore, there are a few states that have only one business in our dataset, which are not suitable for our recommender task, and we have, thus, decided to drop these states with only one business: North Carolina, Colorado, Hawaii, Montana, and South Dakota.\n",
    "\n",
    "The final dataset for the business records has a total of 84 features with 68,054 entries. The business dataset has a total of five numeric features (`latitude`, `longitude`, `stars`, `review_count`, `is_open`). However, since `is_open` is a binary feature and `latitude` and `longitude` are useful in their original states. Only `stars` and `review_count` have been standardized to prevent any potential bias from large scales.\n",
    "\n",
    "\n",
    "**Review:**\n",
    "\n",
    "For the review dataset, we have first filtered the dataset in accordance with the business dataset by filtering out reviews for irrelevant businesses (using `business_id`) that have been pre-processed out as described above. This has reduced the size of the review dataset from 6,990,280 to 5,257,329 entries. Since the review dataset do not have missing entries, no imputation or dropping related to missing values have been conducted. However, we have decided to standardize the numeric features (`stars`, `useful`, `funny`, `cool`) in the review dataset to prevent any larger scales dominating the analysis and leading to a biased result.\n",
    "\n",
    "\n",
    "**User:**\n",
    "\n",
    "For the user dataset, we first filtered the dataset by keeping only the reviewers that exist in the already processed review data as described previously. Note that there is no missing values in this dataset. We also performed feature simplication and engineering: The features `elite` and `friends` are lists of values. In the context of building our recommender system task, the specific values do not matter much, so we aggretated the two variables by transforming them into counts. Aggregating instead of splitting can also potentially reduce overfitting. We also converted `yelping_since` into `membership_days` to represent how long a user has been a member. We used `March 28,2023` as the baseline for \"today\". In addition, we standardized all the numeric features as preparation for our model.\n",
    "\n",
    "Note: the code for data preparation can be found in `business_dat_inspect.ipynb`, `review_data_inspect.ipynb`, and `user_data_inspect.ipynb`."
   ]
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   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "After cleaning the business data as described above, there are 68,054 restaurant-type businesses and 5,257,329 user reviews left in the datasets. Figure 1 shows that the restaurants in the dataset are located in 12 states with over 23% of them in Pennsylvania.\n",
    "\n",
    "<figure>\n",
    "<figcaption><strong>Figure 1</strong></figcaption>\n",
    "<img src=\"Figures/map_numrecords_by_state.png\"  width=\"700\">\n",
    "</figure>"
   ]
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    "The average star (a rating ranging from 1 to 5 with 0.5 increment) of these restaurants is approximately 3.6 with around 75 reviews for each restaurant. "
   ]
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     "text": [
      "There are 68054 business records with 84 total features.\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_index</th>\n",
       "      <th>business_id</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <th>...</th>\n",
       "      <th>music_live</th>\n",
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       "      <th>hours_Friday</th>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>MTSW4McQd7CbVtyjqoe9mw</td>\n",
       "      <td>St Honore Pastries</td>\n",
       "      <td>935 Race St</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>PA</td>\n",
       "      <td>19107</td>\n",
       "      <td>39.955505</td>\n",
       "      <td>-75.155564</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>7:0-20:0</td>\n",
       "      <td>7:0-20:0</td>\n",
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       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>mWMc6_wTdE0EUBKIGXDVfA</td>\n",
       "      <td>Perkiomen Valley Brewery</td>\n",
       "      <td>101 Walnut St</td>\n",
       "      <td>Green Lane</td>\n",
       "      <td>PA</td>\n",
       "      <td>18054</td>\n",
       "      <td>40.338183</td>\n",
       "      <td>-75.471659</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>closed</td>\n",
       "      <td>closed</td>\n",
       "      <td>14:0-22:0</td>\n",
       "      <td>16:0-22:0</td>\n",
       "      <td>12:0-22:0</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>CF33F8-E6oudUQ46HnavjQ</td>\n",
       "      <td>Sonic Drive-In</td>\n",
       "      <td>615 S Main St</td>\n",
       "      <td>Ashland City</td>\n",
       "      <td>TN</td>\n",
       "      <td>37015</td>\n",
       "      <td>36.269593</td>\n",
       "      <td>-87.058943</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0:0-0:0</td>\n",
       "      <td>6:0-22:0</td>\n",
       "      <td>6:0-22:0</td>\n",
       "      <td>6:0-22:0</td>\n",
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       "<p>3 rows × 84 columns</p>\n",
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      "text/plain": [
       "   original_index             business_id                      name  \\\n",
       "0               3  MTSW4McQd7CbVtyjqoe9mw        St Honore Pastries   \n",
       "1               4  mWMc6_wTdE0EUBKIGXDVfA  Perkiomen Valley Brewery   \n",
       "2               5  CF33F8-E6oudUQ46HnavjQ            Sonic Drive-In   \n",
       "\n",
       "         address          city state postal_code   latitude  longitude  stars  \\\n",
       "0    935 Race St  Philadelphia    PA       19107  39.955505 -75.155564    4.0   \n",
       "1  101 Walnut St    Green Lane    PA       18054  40.338183 -75.471659    4.5   \n",
       "2  615 S Main St  Ashland City    TN       37015  36.269593 -87.058943    2.0   \n",
       "\n",
       "   ...  music_live  music_video music_karaoke hours_Monday  hours_Tuesday  \\\n",
       "0  ...        None         None          None     7:0-20:0       7:0-20:0   \n",
       "1  ...        None         None          None       closed         closed   \n",
       "2  ...        None         None          None      0:0-0:0       6:0-22:0   \n",
       "\n",
       "  hours_Wednesday hours_Thursday hours_Friday hours_Saturday hours_Sunday  \n",
       "0        7:0-20:0       7:0-20:0     7:0-21:0       7:0-21:0     7:0-21:0  \n",
       "1       14:0-22:0      16:0-22:0    12:0-22:0      12:0-22:0    12:0-18:0  \n",
       "2        6:0-22:0       6:0-22:0      9:0-0:0       9:0-22:0     8:0-22:0  \n",
       "\n",
       "[3 rows x 84 columns]"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>68054.000000</td>\n",
       "      <td>68054.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.563949</td>\n",
       "      <td>74.619861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.851437</td>\n",
       "      <td>169.900997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>7568.000000</td>\n",
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      "text/plain": [
       "              stars  review_count\n",
       "count  68054.000000  68054.000000\n",
       "mean       3.563949     74.619861\n",
       "std        0.851437    169.900997\n",
       "min        1.000000      5.000000\n",
       "25%        3.000000     11.000000\n",
       "50%        3.500000     27.000000\n",
       "75%        4.000000     73.000000\n",
       "max        5.000000   7568.000000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "busi_df = pd.read_feather(\"data/yelp_business_cleaned.feather\")\n",
    "print(f\"There are {busi_df.shape[0]} business records with {busi_df.shape[1]} total features.\")\n",
    "display(busi_df.head(3))\n",
    "busi_df[['stars', 'review_count']].describe()"
   ]
  },
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    "When looking at the distribution of rating stars, it is slightly left skewed with mode at $4.0$. On the other hand, the highly right skewed distribution of the number of reviews shown below indicates that a small number of restaurants are more popular than the others and have more reviews. Most users tend to rate restaurants between 3.0 and 4.5. Only a small fraction of them give out 1 star."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b1ee26f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of restaurants')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize = (12, 4))\n",
    "sns.histplot(data = busi_df, x = 'review_count', \n",
    "             color = 'cornflowerblue', stat = 'percent',\n",
    "             log_scale = True, ax = ax[0])\n",
    "ax[0].set_title(\"Distribution of Number of Reviews\")\n",
    "ax[0].set_xlabel(\"Number of Reviews (log)\")\n",
    "\n",
    "sns.barplot(data = busi_df.groupby(['stars'])[['business_id']].agg('count').reset_index().astype({'stars': str}),\n",
    "            x = 'stars', \n",
    "            y = 'business_id',\n",
    "            color = 'cornflowerblue', ax = ax[1])\n",
    "ax[1].set_title(\"Distribution of Rating Stars\")\n",
    "ax[1].set_xlabel(\"Rating Stars\")\n",
    "ax[1].set_ylabel(\"Number of restaurants\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fc1ef59c",
   "metadata": {},
   "source": [
    "Still, not all of the restaurants in the dataset are still in operation. About 30% of the businesses have been closed. This fact has an important implication for our recommender system. We may use data from restaurants that are now closed during training to gain more insights into what users like and dislike. However, we will not recommend users to a closed restaurant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b3104d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage (%)')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# proportion of businesses that are still in operation\n",
    "in_oper = busi_df['is_open'].value_counts(normalize = True, sort = True, dropna = False)\n",
    "in_oper = in_oper.to_frame().reset_index()\n",
    "in_oper.rename(columns = {'is_open': 'proportion', 'index' : 'in operation'}, inplace = True)\n",
    "in_oper['in operation'] = in_oper['in operation'].map({1: \"Yes\", 0: \"No\"})\n",
    "in_oper['proportion'] = in_oper['proportion'] * 100\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (3, 3))\n",
    "sns.barplot(data = in_oper,\n",
    "            x = 'in operation', y = \"proportion\", \n",
    "            order = ['Yes', 'No'],\n",
    "            color = \"cornflowerblue\")\n",
    "plt.title(\"Proportion of businesses that are still in operation\")\n",
    "plt.xlabel(\"In Operation\")\n",
    "plt.ylabel(\"Percentage (%)\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ddae426d",
   "metadata": {},
   "source": [
    "The following describes some characteristics of the restaurants in the dataset that users might weigh when choosing a restaurant and hence might be a critical aspect for our recommender system: \n",
    "- More than half of the restaurants price their services below $30 and there are a small number of high-end restaurants (pricing over $61).\n",
    "- In addition, for customers who have cars, over 60 percent of the restaurants indicate that they have at least one parking option (e.g. on street, parking lots, valet, etc.). \n",
    "- We believe that certain users have dietary restrictions and so we look at the special diet types that restaurants offer. The data shows that more than $99\\%$ of restaurants do not indicate their dietary accommodations on Yelp. Yet, for those whom we do have data, gluten-free, vegetarian, and vegan are the top 3 most available options. \n",
    "- There are only about $5\\%$ of restaurants that are dog-friendly. This feature might be important for dog owners who want to eat out and bring their dogs with them. \n",
    "\n",
    "Regardless, we must use these characteristics with caution as the information for the majority of businesses on our dataset is not available.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "096f9646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage (%)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Price ranges\n",
    "label_map = {\"1\": \"under $10\",\n",
    "             \"2\": \"$11-30\",\n",
    "             \"3\": \"$31-60\",\n",
    "             \"4\": \"over $61\",\n",
    "             \"None\": \"N/A\"}\n",
    "priceranges = busi_df['RestaurantsPriceRange2'].map(label_map).value_counts(normalize = True, sort = True, dropna = False)\n",
    "priceranges = priceranges.to_frame().reset_index()\n",
    "priceranges.rename(columns = {'RestaurantsPriceRange2': 'proportion', 'index' : 'price range'}, inplace = True)\n",
    "priceranges['proportion'] = priceranges['proportion'] * 100\n",
    "# priceranges\n",
    "\n",
    "busi_df['has_parking'] = busi_df[['parking_garage', 'parking_street', \n",
    "                        'parking_validated', 'parking_lot', \n",
    "                        'parking_valet']].replace({\"None\": np.nan,\n",
    "                                                \"True\": 1, \n",
    "                                                \"False\": 0}).max(axis = 1)\n",
    "parking_stat = busi_df['has_parking'].value_counts(dropna = False, normalize = True).to_frame().reset_index()\n",
    "parking_stat.rename(columns = {'has_parking': 'proportion', 'index' : 'has_parking'}, inplace = True)\n",
    "parking_stat['has_parking'] = parking_stat['has_parking'].map({1: \"Yes\", 0: \"No\"})\n",
    "parking_stat['has_parking'].fillna(\"N/A\", inplace = True)\n",
    "parking_stat['proportion'] = parking_stat['proportion'] * 100\n",
    "# parking_stat\n",
    "\n",
    "diets = busi_df[['diet_dairy-free', 'diet_gluten-free',\n",
    "                'diet_vegan', 'diet_kosher',\n",
    "                'diet_halal', 'diet_soy-free',\n",
    "                'diet_vegetarian']].replace({\"None\": np.nan,\n",
    "                                        \"True\": 1, \n",
    "                                        \"False\": 0})\n",
    "val_count_list = []\n",
    "for d in list(diets.columns):\n",
    "    count = diets[d].value_counts(dropna = False, normalize = True).to_frame()\n",
    "    val_count_list.append(count)\n",
    "diets_counts = pd.concat(val_count_list, axis = 1)\n",
    "diets_counts.reset_index(inplace = True)\n",
    "diets_counts['index'] = diets_counts['index'].map({1: \"Yes\", 0: \"No\"})\n",
    "diets_counts['index'].fillna(\"N/A\", inplace = True)\n",
    "diets_counts = diets_counts.melt(id_vars = ['index'], \n",
    "                                 var_name = \"diet type\",\n",
    "                                 value_name = 'proportion'\n",
    "                                 )\n",
    "diets_counts = diets_counts[diets_counts['index'] != \"N/A\"]\n",
    "diets_counts['proportion'] = diets_counts['proportion'] * 100\n",
    "diets_counts['diet type'] = diets_counts['diet type'].str.replace(\"diet_\", \"\")\n",
    "diets_counts.sort_values([\"index\", \"proportion\"], ascending = [False, False], inplace = True)\n",
    "# diets_counts\n",
    "\n",
    "dog_allow = busi_df['DogsAllowed'].value_counts(normalize = True, sort = True, dropna = False)\n",
    "dog_allow = dog_allow.to_frame().reset_index()\n",
    "dog_allow.rename(columns = {'DogsAllowed': 'proportion', 'index' : 'Allowing dogs'}, inplace = True)\n",
    "dog_allow['Allowing dogs'] = dog_allow['Allowing dogs'].map({\"True\": \"Yes\", \"False\": \"No\", \"None\": \"N/A\"})\n",
    "dog_allow['proportion'] = dog_allow['proportion'] * 100\n",
    "# dog_allow\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(2, 2, figsize = (12, 10))\n",
    "ax = ax.ravel()\n",
    "sns.barplot(data = priceranges,\n",
    "            y = 'price range', x = \"proportion\", \n",
    "            order = [\"under $10\", \"$11-30\", \"$31-60\", \"over $61\", \"N/A\"],\n",
    "            color = \"cornflowerblue\", ax = ax[0])\n",
    "ax[0].set_title(\"Proportion of businesses by Price Ranges\")\n",
    "ax[0].set_ylabel(\"Price Range\")\n",
    "ax[0].set_xlabel(\"Percentage (%)\")\n",
    "\n",
    "sns.barplot(data = parking_stat,\n",
    "            x = 'has_parking', y = \"proportion\",\n",
    "            color = \"cornflowerblue\", ax = ax[1])\n",
    "ax[1].set_title(\"Proportion of businesses by parking availability\")\n",
    "ax[1].set_xlabel(\"Parking Availability\")\n",
    "ax[1].set_ylabel(\"Percentage\")\n",
    "\n",
    "sns.barplot(data = diets_counts,\n",
    "            x = 'proportion', y = \"diet type\",\n",
    "            hue = 'index',\n",
    "            palette = [\"cornflowerblue\", \"wheat\"], ax = ax[2])\n",
    "ax[2].set_title(\"Proportion of special diet types offered\")\n",
    "ax[2].set_xlabel(\"Percentage (%)\")\n",
    "ax[2].set_ylabel(\"Diet Type\")\n",
    "ax[2].legend(title = \"\")\n",
    "\n",
    "sns.barplot(data = dog_allow[dog_allow['Allowing dogs'] != \"N/A\"],\n",
    "            x = 'Allowing dogs', y = \"proportion\", \n",
    "            order = ['Yes', 'No'],\n",
    "            color = \"cornflowerblue\", ax = ax[3])\n",
    "ax[3].set_title(\"Proportion of restaurants that allow dogs\")\n",
    "ax[3].set_xlabel(\"Allowing Dogs\")\n",
    "ax[3].set_ylabel(\"Percentage (%)\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "766a2a8d",
   "metadata": {},
   "source": [
    "As for the 5,257,329 user review data, the average of all standardized numeric variables (`stars`, `useful`,`funny`,`cool`) are all close to 0. The only numerical distinction between these features are that they have distinct minimum and maximum values. This might be due to the overall distribution of each numerical value being extremely right skewed, meaning that only a few reviews have received high numbers of useful, funny, and cool votes from other users. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d9a479d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 5257329 review records with 9 total features.\n"
     ]
    },
    {
     "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>review_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>cool</th>\n",
       "      <th>text</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KU_O5udG6zpxOg-VcAEodg</td>\n",
       "      <td>mh_-eMZ6K5RLWhZyISBhwA</td>\n",
       "      <td>XQfwVwDr-v0ZS3_CbbE5Xw</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>If you decide to eat here, just be aware it is...</td>\n",
       "      <td>2018-07-07 22:09:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>saUsX_uimxRlCVr67Z4Jig</td>\n",
       "      <td>8g_iMtfSiwikVnbP2etR0A</td>\n",
       "      <td>YjUWPpI6HXG530lwP-fb2A</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Family diner. Had the buffet. Eclectic assortm...</td>\n",
       "      <td>2014-02-05 20:30:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AqPFMleE6RsU23_auESxiA</td>\n",
       "      <td>_7bHUi9Uuf5__HHc_Q8guQ</td>\n",
       "      <td>kxX2SOes4o-D3ZQBkiMRfA</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Wow!  Yummy, different,  delicious.   Our favo...</td>\n",
       "      <td>2015-01-04 00:01:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                review_id                 user_id             business_id  \\\n",
       "0  KU_O5udG6zpxOg-VcAEodg  mh_-eMZ6K5RLWhZyISBhwA  XQfwVwDr-v0ZS3_CbbE5Xw   \n",
       "1  saUsX_uimxRlCVr67Z4Jig  8g_iMtfSiwikVnbP2etR0A  YjUWPpI6HXG530lwP-fb2A   \n",
       "2  AqPFMleE6RsU23_auESxiA  _7bHUi9Uuf5__HHc_Q8guQ  kxX2SOes4o-D3ZQBkiMRfA   \n",
       "\n",
       "   stars  useful  funny  cool  \\\n",
       "0      3       0      0     0   \n",
       "1      3       0      0     0   \n",
       "2      5       1      0     1   \n",
       "\n",
       "                                                text                date  \n",
       "0  If you decide to eat here, just be aware it is... 2018-07-07 22:09:11  \n",
       "1  Family diner. Had the buffet. Eclectic assortm... 2014-02-05 20:30:30  \n",
       "2  Wow!  Yummy, different,  delicious.   Our favo... 2015-01-04 00:01:03  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>stars</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>cool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.257329e+06</td>\n",
       "      <td>5.257329e+06</td>\n",
       "      <td>5.257329e+06</td>\n",
       "      <td>5.257329e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.804201e+00</td>\n",
       "      <td>1.021837e+00</td>\n",
       "      <td>3.186590e-01</td>\n",
       "      <td>5.057937e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.393488e+00</td>\n",
       "      <td>2.792205e+00</td>\n",
       "      <td>1.728878e+00</td>\n",
       "      <td>2.204489e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>4.200000e+02</td>\n",
       "      <td>7.920000e+02</td>\n",
       "      <td>4.040000e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              stars        useful         funny          cool\n",
       "count  5.257329e+06  5.257329e+06  5.257329e+06  5.257329e+06\n",
       "mean   3.804201e+00  1.021837e+00  3.186590e-01  5.057937e-01\n",
       "std    1.393488e+00  2.792205e+00  1.728878e+00  2.204489e+00\n",
       "min    1.000000e+00 -1.000000e+00 -1.000000e+00 -1.000000e+00\n",
       "25%    3.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00\n",
       "50%    4.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00\n",
       "75%    5.000000e+00  1.000000e+00  0.000000e+00  0.000000e+00\n",
       "max    5.000000e+00  4.200000e+02  7.920000e+02  4.040000e+02"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_re = pd.read_feather(\"data/yelp_review_cleaned.feather\")\n",
    "print(f\"There are {df_re.shape[0]} review records with {df_re.shape[1]} total features.\")\n",
    "display(df_re.head(3))\n",
    "df_re[['stars', 'useful','funny','cool']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f0180ade",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The mean stars is 3.8042 \n",
      "The mean number of useful is 1.0218 \n",
      "The mean number of funny is 0.3187 \n",
      "The mean number of cool is 0.5058 \n",
      "The minimum and maximum values of stars is 1.0000 and 5.0000\n",
      "The minimum and maximum values of useful is -1.0000 and 420.0000\n",
      "The minimum and maximum values of funny is -1.0000 and 792.0000\n",
      "The minimum and maximum values of cool is -1.0000 and 404.0000\n"
     ]
    }
   ],
   "source": [
    "mean_stars = df_re[\"stars\"].mean() \n",
    "mean_useful = df_re[\"useful\"].mean()\n",
    "mean_funny = df_re['funny'].mean()\n",
    "mean_cool = df_re['cool'].mean()\n",
    "\n",
    "min_stars = df_re['stars'].min()\n",
    "min_useful = df_re['useful'].min()\n",
    "min_funny = df_re['funny'].min()\n",
    "min_cool = df_re['cool'].min()\n",
    "\n",
    "max_stars = df_re['stars'].max()\n",
    "max_useful = df_re['useful'].max()\n",
    "max_funny = df_re['funny'].max()\n",
    "max_cool = df_re['cool'].max()\n",
    "\n",
    "print(\n",
    "    \"\\n\"\n",
    "    f\"The mean stars is {mean_stars:.4f} \\n\"\n",
    "    f\"The mean number of useful is {mean_useful:.4f} \\n\"\n",
    "    f\"The mean number of funny is {mean_funny:.4f} \\n\"\n",
    "    f\"The mean number of cool is {mean_cool:.4f} \\n\"\n",
    "    f\"The minimum and maximum values of stars is {min_stars:.4f} and {max_stars:.4f}\\n\"\n",
    "    f\"The minimum and maximum values of useful is {min_useful:.4f} and {max_useful:.4f}\\n\"\n",
    "    f\"The minimum and maximum values of funny is {min_funny:.4f} and {max_funny:.4f}\\n\"\n",
    "    f\"The minimum and maximum values of cool is {min_cool:.4f} and {max_cool:.4f}\"\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "051395a8",
   "metadata": {},
   "source": [
    "Since `useful`, `funny`, `cool` features represent number of designated votes, we may notice that these numeric are correlated. From the correlation plot, `useful` and `cool` seem to be more correlated than other features and `stars` have little correlations with three other variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8fc488e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/df/y6mck5f93m5_f04v7_qhxmp40000gn/T/ipykernel_75473/4131803778.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  cor = df_re.corr()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor = df_re.corr()\n",
    "sns.heatmap(cor, cmap = 'Blues')\n",
    "plt.title(\"Review Dataset Correlation Map\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "60bee50c",
   "metadata": {},
   "source": [
    "Below figures are to check the distribution of reviews by business, users, and states. The figures have been investigated by counting the numbers of reviews by distinct `user_id` and by `state` on corresponding the `business_id` after merging the columns of state, city, name, for the filtered business dataset (i.e. restaurants). By doing this, we can understand that there is an imbalance for the review dataset since some users have written more reviews than other users and some restaurants have received much larger quantities of reviews than others. \n",
    "\n",
    "Following information have been obtained from the plots:\n",
    "* A total of 1,532,233 users have written the 5,257,329 reviews on businesses in the dataset. \n",
    "* There are 76,869 users who have written more than 10 reviews on businesses. \n",
    "* Pennsylvania has the largest amount of reviews from the users on businesses, whereas Illinois has the smallest amount of reviews among the states we have in the dataset."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cfac14a4",
   "metadata": {},
   "source": [
    "#### By business"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c35e2f86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 68054 businesses received reviews.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group by unique business id\n",
    "review_count_by_business = df_re.groupby('business_id')['review_id'].count().sort_values(ascending=False)\n",
    "plt.bar(np.arange(review_count_by_business.shape[0]), review_count_by_business);\n",
    "plt.title('Review Count by Business')\n",
    "plt.xlabel('Number of Reviews')\n",
    "plt.ylabel(\"Counts (number of businesses)\")\n",
    "print(f\"There are {len(review_count_by_business)} businesses received reviews.\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "af8bf7ff",
   "metadata": {},
   "source": [
    "#### By states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87ffeff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 5257329 review records with 12 total features.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# merge business dataset to get state, city, name\n",
    "business = pd.read_feather(\"data/yelp_business_cleaned.feather\")\n",
    "merge = pd.merge(df_re, business, on='business_id', how='outer')\n",
    "merge = merge[['review_id', 'user_id', 'business_id', 'text', 'date', 'stars_x',\n",
    "       'useful', 'funny', 'cool','state','city','name']].rename(columns={'stars_x':'stars'})\n",
    "print(f\"There are {merge.shape[0]} review records with {merge.shape[1]} total features.\")\n",
    "\n",
    "# group by state in descending order\n",
    "state_count_by_business = merge.groupby('state')['business_id'].count().sort_values(ascending=False)\n",
    "\n",
    "# Pie plot to see the portion as well\n",
    "plt.figure(figsize=(10,8))\n",
    "colors = sns.color_palette('bright')[0:14]\n",
    "plt.pie(state_count_by_business.array, labels=state_count_by_business.index, colors = colors, autopct='%.0f%%')\n",
    "plt.title(\"Reviews Distribution by State\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b25fa23c",
   "metadata": {},
   "source": [
    "#### By users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a85f6f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1532233 users have written the 5257329 reviews on business in the dataset.\n",
      "There are 76869 users who have written more than 10 reviews on businesses.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group by user id in descending order\n",
    "user_count = merge.groupby(by=[\"user_id\"])['business_id'].count().sort_values(ascending=False)\n",
    "# checking unique users\n",
    "print(f\"{len(user_count)} users have written the {merge.shape[0]} reviews on business in the dataset.\")\n",
    "\n",
    "# checking unique users with more than 10 reviews written\n",
    "user_count = user_count.to_frame()\n",
    "users_aboveten = user_count[user_count['business_id'] > 10]['business_id']\n",
    "print(f\"There are {users_aboveten.shape[0]} users who have written more than 10 reviews on businesses.\")\n",
    "\n",
    "# Plot the counts of reviews by users\n",
    "plt.bar(np.arange(users_aboveten.shape[0]), users_aboveten);\n",
    "plt.title('Review Counts by Users who have written reviews more than 10 times')\n",
    "plt.xlabel('Number of Reviews')\n",
    "plt.ylabel(\"Counts (number of users)\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e786e3f6",
   "metadata": {},
   "source": [
    "There is an obvious imbalance in the review dataset. However, mitigating class imbalance in this dataset can be considered improper since the number of reviews could be an indicator for the business’s popularity. Based on this assumption, both under-sampling of restaurants with more reviews and over-sampling (e.g. SMOTE) of restaurants with less reviews are inappropriate. Due to this reason, the class imbalance has been preserved for the review dataset."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d874cc81-9b2a-402a-a8ef-d6ae6739d4d0",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "Recommender systems are essentially information filtering systems which leverage statistical and machine learning models to learn user data (e.g. their past behaviors) and predict their current interests and preferences. There are roughly three main approaches to build a recommender system:\n",
    "\n",
    "1. **Collaborative filtering**: suggestions are based solely on the similarities among users, or, alternatively, among items. \n",
    "2. **Content-based filtering**: suggestions are based on additional information such as a description of the item and a profile of the user's preferences.\n",
    "3. **Hybrid**: a combination of the previous two approaches. \n",
    "\n",
    "Note that the collaborative filtering techniques can also be divided into two categories: **memory-based** and **model-based**. A memory-based approach makes predictions based solely on *memory* of past records by finding similarities or neighborhoods in users or items. A method-based approach assumes an underlying model to make predictions. \n",
    "\n",
    "Additionally, collaborative filtering can be further broken down into two types: **user-based** and **item-based**. The user-based collaborative filtering approach is based on an assumption that some users tend to share the same preferences. It basically group users into clusters given the information on ratings a user gave out. On the other hand, the item-based collaborative filtering makes predictions based on similarities among items that the users have previously rated. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aac336d7-d7e6-4219-8600-72b501f0371b",
   "metadata": {},
   "source": [
    "### Baseline Model\n",
    "\n",
    "For this project, we choose the memory based collaborative filtering method as our baseline model, since the collaborative filtering is well known for its simplicity and, yet, effectiveness. \n",
    "To create the baseline model, we will create a user-item matrix, in which the rows represent users and columns represent the items. From here, we can then make predictions after calculating similarities among items. To facilitate our implementation, we will rely on the recommendation system package [`scikit-surprise`](https://surpriselib.com/), particularly the `KNNWithMeans` method which takes into account the mean ratings of each user.\n",
    "\n",
    "We will explore both user-based and item-based approaches and try out different similarity metrics such as Mean Squared Difference (MSD) and cosine similarity. Note that the MSD similarity can be computed as follows:\n",
    "\n",
    "$$msd\\_sim(u, v) = \\frac{1}{msd(u, v) + 1} \\text{ where } msd(u,v) = \\frac{1}{|I_{uv}|} \\cdot \\sum_{i \\in I_{uv}} (r_{ui} - r_{vi})^2, $$\n",
    "\n",
    "$I_{uv}$ is the set of all items rated by both users $u$ and $v$, $r_{ui}$ the true rating of user $u$ for item $i$.\n",
    "\n",
    "First, import the cleaned dataset with 3 columns: `user_id`, `business_id`,  and`stars` where  each row corresponds to a user's rating for a particular restaurant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdf862f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "review_df = pd.read_feather('data/yelp_review_cleaned.feather')\n",
    "df = review_df.loc[:, ['user_id', 'business_id', 'stars']]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "34fe0f7f",
   "metadata": {},
   "source": [
    "Due to resource limitation, we will sample only 10K records for our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d180c6d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068250</th>\n",
       "      <td>GyFJNSJjI5aWww-D0Btcbw</td>\n",
       "      <td>GlKffg2PMtzByocI5OHIQA</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371839</th>\n",
       "      <td>o66iBwIWxfWPypnqfrHVNw</td>\n",
       "      <td>XVFUNtPWYpxhoWPtBQHFdQ</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        user_id             business_id  stars\n",
       "1322294  0lpxU4Dfi8AeBt0SeCrEuw  tQKqrLs16Xi-lFrd3_CBAQ      1\n",
       "4297632  5nw1Zc3fi_ehDJFd3mUEYA  nLxNJuvgoHQHn_IGYifRnw      1\n",
       "2143059  7fDqaGdUMccXQ4bnPwR6yg  etaIhl-sduOKc6J_qHmmtA      3\n",
       "3068250  GyFJNSJjI5aWww-D0Btcbw  GlKffg2PMtzByocI5OHIQA      3\n",
       "1371839  o66iBwIWxfWPypnqfrHVNw  XVFUNtPWYpxhoWPtBQHFdQ      2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub = df.sample(10000, random_state = 42)\n",
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2ca75c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.knns.KNNWithMeans at 0x3c5833f70>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in data into scikit-surprise format\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(sub, reader)\n",
    "\n",
    "# configs\n",
    "sim_options = {\n",
    "    \"name\": \"cosine\",  # to use item-based cosine similarity\n",
    "    \"user_based\": False,  # Compute similarities between items\n",
    "}\n",
    "\n",
    "algo = KNNWithMeans(sim_options=sim_options)\n",
    "\n",
    "trainingSet = data.build_full_trainset()\n",
    "algo.fit(trainingSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b202c46e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict some samples\n",
    "prediction = algo.predict(sub.iloc[4, 0], sub.iloc[4, 1])\n",
    "prediction.est"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "79b4faea",
   "metadata": {},
   "source": [
    "Here, we perform a hyperparameter grid search to optimize prediction errors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa4cf7f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    }
   ],
   "source": [
    "sim_options = {\n",
    "    \"name\": [\"msd\", \"cosine\"],\n",
    "    \"min_support\": [3, 4, 5],\n",
    "    \"user_based\": [False, True],\n",
    "}\n",
    "\n",
    "param_grid = {\"sim_options\": sim_options}\n",
    "\n",
    "gs = GridSearchCV(KNNWithMeans, param_grid, measures=[\"rmse\", \"mse\", \"mae\"], cv=3)\n",
    "gs.fit(data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "73ab93f2",
   "metadata": {},
   "source": [
    "The best model has the MSE of 1.98 using the Mean Squared Difference similarity metric, minimum number of common items of 3, and user-based approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0484168-beac-414a-9a9b-640519d58949",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best rmse: 1.9810003243914922\n",
      "best params: {'sim_options': {'name': 'msd', 'min_support': 3, 'user_based': False}}\n"
     ]
    }
   ],
   "source": [
    "print(f'best rmse: {gs.best_score[\"mse\"]}')\n",
    "print(f'best params: {gs.best_params[\"mse\"]}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c5109aab",
   "metadata": {},
   "source": [
    "It is important to note that this type of collaborative filtering may suffer from three problems: data sparsity, scalability, and cold start. Oftentimes, the data is sparse, meaning that a user has rates only a small number of restaurants while there are thousands of restaurants avaiable in the dataset. The use of a user-item matrix also makes it difficult to scale up as the size of the data increases. Most importantly, because this approach relies on past information, it suffers from cold start problem where it cannot predict given a new user or item due to the lack of prior information."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4808cf61-f408-43cc-a626-4d215c7f0712",
   "metadata": {},
   "source": [
    "### Neural Collaborative Filtering (NCF) Model\n",
    "\n",
    "The baseline model, which depends a 2-dimensional user-item interaction matrix, works by simply calculating similarity scores using a certain similarity metric and recommends items or restaurants based on these similarities. Now, we will create a more robust model by perfoming the following:\n",
    "\n",
    "- Extend the 2D user-item matrix into a four-dimensional tensor by attaching user features as well as item/restaurant features. \n",
    "- Train the model using a neural network architecture.\n",
    "\n",
    "This method is known as the **neural collaborative filtering (NCF)** approach. He et al. (2017) empirically shows that using deeper layers of neural networks provides better recommendation performance. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "91c7637e-3bac-4dbf-bc1f-2c8e67220ee6",
   "metadata": {},
   "source": [
    "We will now load in datasets from all necessary sources: users, restaurants, and reviews:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6ccd2c63-4814-4aea-aa0e-5182439627b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.8 s, sys: 8.42 s, total: 14.2 s\n",
      "Wall time: 38.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "user_df = pd.read_feather('data/yelp_user_cleaned.feather')  # 100 MB\n",
    "business_df = pd.read_feather('data/yelp_business_cleaned.feather')  # 40 MB\n",
    "review_df = pd.read_feather('data/yelp_review_cleaned.feather')  # 2.3 GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d58220b8-132a-4cf4-91f8-76605a44da70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1532223,), (1532233,))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_df.user_id.unique().shape, review_df.user_id.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "825c2283-d140-4f91-b15e-f2e9b12bd503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((68054,), (68054,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.business_id.unique().shape, review_df.business_id.unique().shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3422d6b4-c242-4c02-811f-8b881c9c159d",
   "metadata": {},
   "source": [
    "Due to resource limitation, we sample 100K review records for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2277ec81-27f0-4107-9076-8508ae944ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "review_df = review_df.sample(100_000, random_state=42)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0ce77a53-0edd-4831-8ce0-d9a90f5caf5a",
   "metadata": {},
   "source": [
    "We use the review dataset to build our user-item matrix. Note that previously we only kept these columns for building user-item matrix: `['user_id', 'business_id', 'stars']`, but for the NCF model we include additional features from review data: `['useful', 'funny', 'cool']`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fd59946b-77ed-4910-aa2e-814cc39f53e4",
   "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>user_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>cool</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1322294</th>\n",
       "      <td>0lpxU4Dfi8AeBt0SeCrEuw</td>\n",
       "      <td>tQKqrLs16Xi-lFrd3_CBAQ</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>My friends and I went there on a Friday night ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4297632</th>\n",
       "      <td>5nw1Zc3fi_ehDJFd3mUEYA</td>\n",
       "      <td>nLxNJuvgoHQHn_IGYifRnw</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Clean, friendly waitstaff.  The food, well, to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2143059</th>\n",
       "      <td>7fDqaGdUMccXQ4bnPwR6yg</td>\n",
       "      <td>etaIhl-sduOKc6J_qHmmtA</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Super swanky lunch spot in Clayton.  I love, l...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        user_id             business_id  stars  useful  funny  \\\n",
       "1322294  0lpxU4Dfi8AeBt0SeCrEuw  tQKqrLs16Xi-lFrd3_CBAQ      1       2      0   \n",
       "4297632  5nw1Zc3fi_ehDJFd3mUEYA  nLxNJuvgoHQHn_IGYifRnw      1       1      0   \n",
       "2143059  7fDqaGdUMccXQ4bnPwR6yg  etaIhl-sduOKc6J_qHmmtA      3       2      0   \n",
       "\n",
       "         cool                                               text  \n",
       "1322294     0  My friends and I went there on a Friday night ...  \n",
       "4297632     0  Clean, friendly waitstaff.  The food, well, to...  \n",
       "2143059     2  Super swanky lunch spot in Clayton.  I love, l...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_df = review_df.loc[:, ['user_id', 'business_id', 'stars', 'useful', 'funny', 'cool', 'text']]\n",
    "review_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aee2062a-ec20-4f6d-8f5e-dbca908c22b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_df.drop(['name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7190f7c0-5484-4c2e-a2fb-2b8929e72125",
   "metadata": {},
   "outputs": [],
   "source": [
    "# why we rename it here and not use the suffixes for merging:\n",
    "# we will later need to know which features are related to users\n",
    "# keep a list of column names here is more convenient\n",
    "user_df.rename({'useful': 'useful_user',\n",
    "                'funny': 'funny_user',\n",
    "                'cool': 'cool_user',\n",
    "                'review_count': 'review_count_user'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e6c427bc-1e4c-44f1-8156-f7f53e56c2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# note: we've already expanded the dictionaries such as attributes in business_data_inspect.ipynb\n",
    "business_df.drop(['original_index', 'name', 'address', 'city', 'state',\n",
    "                  'postal_code', 'latitude', 'longitude',\n",
    "                  'attributes', 'categories', 'is_restaurant', 'GoodForMeal', 'BestNights'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "64d6f792-1a52-46ce-90fd-4b8bf1867f17",
   "metadata": {},
   "outputs": [],
   "source": [
    "business_df.rename({'stars': 'stars_business',\n",
    "                    'review_count': 'review_count_business'}, axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "30dd3722-08a0-4f1c-96d1-df4c416e0127",
   "metadata": {},
   "source": [
    "One-hot encode categorical features (except IDs) in the business dataset, and merge all three dataframes together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b221fdd-a6b1-46f5-8300-65907aa97970",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(business_df.drop('business_id', axis=1), drop_first=True)\n",
    "business_df = pd.concat([business_df.loc[:, ['business_id']], dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f395548f-8b30-4214-b6c9-75e66a67e771",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(review_df, user_df, on='user_id')\n",
    "df = pd.merge(df, business_df, on='business_id')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "53d22fab-2cf0-4907-ba45-67c8e5e61c24",
   "metadata": {},
   "source": [
    "For this part, we exclude the review text from our model training. We will include it back in for our model extension below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3e01c8e9-b78c-4481-ba0a-a854763a33b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('text', axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "020a62e3-5874-4e01-a041-67d98f3c2fe2",
   "metadata": {},
   "source": [
    "Again, we sample 10K records due to resource limitation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0ab93dd2-fc62-490f-b377-17c3721bd32b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 7470)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sample(10_000, random_state=42).reset_index(drop=True)\n",
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "33771437-8d6b-41c9-b090-f076ef62f9a5",
   "metadata": {},
   "source": [
    "Filter out unneeded rows in `user_df`, `business_df`, and `review_df` to match `df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "46052de6-d65a-496c-a185-3670a4c03607",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_df = user_df.loc[user_df['user_id'].isin(df['user_id']), :].copy().reset_index(drop=True)\n",
    "business_df = business_df.loc[business_df['business_id'].isin(df['business_id']), :].copy().reset_index(drop=True)\n",
    "review_df = review_df.loc[(review_df['user_id'].isin(df['user_id'])) & \\\n",
    "                    (review_df['business_id'].isin(df['business_id'])), :].copy().reset_index(drop=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9cb9523d-1b1f-47da-b38e-09f0ec1858cf",
   "metadata": {},
   "source": [
    "As mentioned above, we remove `text` from `review_df` for now, but save it for the later extension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4c9297b5-0915-4d44-9369-54c1cc3e195f",
   "metadata": {},
   "outputs": [],
   "source": [
    "review_text = review_df['text'].copy()\n",
    "review_df.drop('text', axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "be5e0bda-5126-46eb-98b5-41383b22b1e6",
   "metadata": {},
   "source": [
    "Before model training, we have to encode `user_id` and `business_id` to convert string into integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5c554003-ef6e-4e87-ae1d-f5dccb1b73ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_id_encoder = LabelEncoder()\n",
    "business_id_encoder = LabelEncoder()\n",
    "\n",
    "user_df['user_id'] = user_id_encoder.fit_transform(user_df['user_id'])\n",
    "review_df['user_id'] = user_id_encoder.transform(review_df['user_id'])\n",
    "business_df['business_id'] = business_id_encoder.fit_transform(business_df['business_id'])\n",
    "review_df['business_id'] = business_id_encoder.transform(review_df['business_id'])\n",
    "\n",
    "df['user_id'] = user_id_encoder.transform(df['user_id'])\n",
    "df['business_id'] = business_id_encoder.transform(df['business_id'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b9089c54-58c7-4900-9e85-9114621e66a4",
   "metadata": {},
   "source": [
    "We split train and test sets with the test size of 20% and scale the numerical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "15c3003b-1cbe-4efb-a847-c3b84be59650",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "90db75ed-a46f-4c26-87e6-6a658a91f10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_scaling(df_train, df_test):\n",
    "    # save stars (target, do not scale)\n",
    "    stars_train = df_train['stars'].copy()\n",
    "    stars_test = df_test['stars'].copy()\n",
    "\n",
    "    # scale features\n",
    "    scaler = MinMaxScaler()\n",
    "    df_train = scaler.fit_transform(df_train)\n",
    "    df_test = scaler.transform(df_test)\n",
    "\n",
    "    # convert back to data frames\n",
    "    df_train = pd.DataFrame(df_train, columns=df.columns)\n",
    "    df_test = pd.DataFrame(df_test, columns=df.columns)\n",
    "\n",
    "    # restore stars\n",
    "    df_train['stars'] = stars_train.values\n",
    "    df_test['stars'] = stars_test.values\n",
    "\n",
    "    return df_train, df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ca1960a7-6799-4e38-9caf-5c22745b3bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = feature_scaling(df_train, df_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1cb4ae54-0c78-44c1-b97d-d39eb8483c5b",
   "metadata": {},
   "source": [
    "We build a neural network model following the architecture drawn below in Figure 2:\n",
    "\n",
    "<figure>\n",
    "<figcaption><strong>Figure 2</strong></figcaption>\n",
    "<img src=\"Figures/model_architecture.png\"  width=\"700\">\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "358d6869-7dee-4985-8553-c23cc2a8a6b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(df=df, user_df=user_df, business_df=business_df):\n",
    "    # helper constants\n",
    "    num_users = df['user_id'].nunique()\n",
    "    num_businesses = df['business_id'].nunique()\n",
    "    \n",
    "    # Model architecture:\n",
    "    \n",
    "    # embeddings for id's\n",
    "    user_input = Input(shape=(1,), name='user_input')\n",
    "    user_embedding = Embedding(num_users, 16, name='user_embedding')(user_input)\n",
    "    user_flatten = Flatten(name='user_flatten')(user_embedding)\n",
    "\n",
    "    business_input = Input(shape=(1,), name='business_input')\n",
    "    business_embedding = Embedding(num_businesses, 16, name='business_embedding')(business_input)\n",
    "    business_flatten = Flatten(name='business_flatten')(business_embedding)\n",
    "\n",
    "    dot_product = Dot(axes=1, name='dot_product')([user_flatten, business_flatten])\n",
    "\n",
    "    # add in user and business features\n",
    "    user_features_input = Input(shape=(user_df.shape[1] - 1,), name='user_features_input')\n",
    "    business_features_input = Input(shape=(business_df.shape[1] - 1,), name='business_features_input')\n",
    "    concat_features = Concatenate(name='concat_features')([dot_product, user_features_input, business_features_input])\n",
    "\n",
    "    dense_layer = Dense(64, activation='relu', name='dense_layer')(concat_features)\n",
    "    output = Dense(1, activation='linear', name='output')(dense_layer)\n",
    "\n",
    "    model = Model(inputs=[user_input, business_input, user_features_input, business_features_input], outputs=output)\n",
    "    model.compile(optimizer=Adam(0.0001), loss='mean_squared_error')\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "67335e7d-4d67-45e1-8b05-c1b565135398",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "eac842de-2ba7-4214-b2eb-abf71379c82b",
   "metadata": {},
   "source": [
    "Train the model using 15 epochs and validation split of size 10%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cb11cda5-9cdf-41bc-8e40-3a6168cb9794",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, df_train=df_train, user_df=user_df, business_df=business_df):\n",
    "    train_inputs = [\n",
    "        df_train['user_id'].values,\n",
    "        df_train['business_id'].values,\n",
    "        df_train[user_df.columns[1:]].values,\n",
    "        df_train[business_df.columns[1:]].values\n",
    "    ]\n",
    "\n",
    "    model.fit(train_inputs, df_train['stars'].values, epochs=15, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5f8df035-dd99-4577-8e68-388afa5c33c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-07 02:56:18.139251: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225/225 [==============================] - 4s 17ms/step - loss: 4.9440 - val_loss: 2.0735\n",
      "Epoch 2/15\n",
      "225/225 [==============================] - 1s 3ms/step - loss: 1.9362 - val_loss: 1.9881\n",
      "Epoch 3/15\n",
      "225/225 [==============================] - 1s 3ms/step - loss: 1.8497 - val_loss: 1.9161\n",
      "Epoch 4/15\n",
      "225/225 [==============================] - 1s 2ms/step - loss: 1.7765 - val_loss: 1.8627\n",
      "Epoch 5/15\n",
      "225/225 [==============================] - 1s 3ms/step - loss: 1.7116 - val_loss: 1.7947\n",
      "Epoch 6/15\n",
      "225/225 [==============================] - 1s 3ms/step - loss: 1.6506 - val_loss: 1.7503\n",
      "Epoch 7/15\n",
      "225/225 [==============================] - 1s 3ms/step - loss: 1.5941 - val_loss: 1.7067\n",
      "Epoch 8/15\n",
      "225/225 [==============================] - 1s 3ms/step - loss: 1.5420 - val_loss: 1.6788\n",
      "Epoch 9/15\n",
      "225/225 [==============================] - 1s 4ms/step - loss: 1.4918 - val_loss: 1.6359\n",
      "Epoch 10/15\n",
      "225/225 [==============================] - 1s 4ms/step - loss: 1.4483 - val_loss: 1.6079\n",
      "Epoch 11/15\n",
      "225/225 [==============================] - 1s 4ms/step - loss: 1.4013 - val_loss: 1.5800\n",
      "Epoch 12/15\n",
      "225/225 [==============================] - 1s 3ms/step - loss: 1.3575 - val_loss: 1.5736\n",
      "Epoch 13/15\n",
      "225/225 [==============================] - 1s 3ms/step - loss: 1.3215 - val_loss: 1.5328\n",
      "Epoch 14/15\n",
      "225/225 [==============================] - 1s 2ms/step - loss: 1.2824 - val_loss: 1.5006\n",
      "Epoch 15/15\n",
      "225/225 [==============================] - 1s 2ms/step - loss: 1.2459 - val_loss: 1.4802\n",
      "CPU times: user 23 s, sys: 7.02 s, total: 30 s\n",
      "Wall time: 14.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_model(model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "887710d9-a8e4-46da-af51-80837d65ee10",
   "metadata": {},
   "source": [
    "We evaluate our model on the test set and get a mean square error of 1.404, or equivalently RMSE of 1.185. Recall that our baseline model has RMSE of 1.409. Thus, the neural collaborative filtering approach with the additional user and restaurant features clearly improves the performance of a recommender system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e44e3fb6-e450-433d-af26-9ab45559e8fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(model, df_test=df_test, user_df=user_df, business_df=business_df):\n",
    "    test_inputs = [\n",
    "        df_test['user_id'].values,\n",
    "        df_test['business_id'].values,\n",
    "        df_test[user_df.columns[1:]].values,\n",
    "        df_test[business_df.columns[1:]].values\n",
    "    ]\n",
    "\n",
    "    test_loss = model.evaluate(test_inputs, df_test['stars'].values)\n",
    "    print(f'Test loss (MSE): {round(test_loss, 3)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "36ecff84-6e58-49ff-8fae-057d907857fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 738us/step - loss: 1.4037\n",
      "Test loss (MSE): 1.404\n"
     ]
    }
   ],
   "source": [
    "test_model(model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6c68a947-210d-4417-b6d9-9c5e144ad290",
   "metadata": {},
   "source": [
    "#### Model Extension 1: Cluster Labels"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6ebd34d7-de5d-4a76-889d-9b4cf97e077e",
   "metadata": {},
   "source": [
    "We would like to improve the performance of our model even further by adding in new features to the training set. In this section, we will perform clustering on users and businesses. The cluster labels will be used as an additional feature. Note that this method is called a mixed cluster network."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9da84d22-58cd-465e-b753-601056e05573",
   "metadata": {},
   "source": [
    "First, we prepare our data for this model by scaling `user_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bced6409-e817-4053-ab62-95c8220d0c00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale(df):\n",
    "    scaler = MinMaxScaler()\n",
    "    return scaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "199d82e2-a7e8-480d-b8f0-33245b3321cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_df_scaled = scale(user_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "25a8ad0d-04f0-46ba-b747-23b18b44c801",
   "metadata": {},
   "source": [
    "And apply dimensionality reduction using PCA of 10 components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "888841cc-ddba-4a2d-baca-520060003626",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pca(df_scaled):\n",
    "    pca = PCA(n_components=10)\n",
    "    return pca.fit_transform(df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8f0abf61-d1c2-47e6-8013-067bf92d8d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_df_pca = pca(user_df_scaled)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "34f9d39f-f4d2-4ed0-809b-05f989f76f55",
   "metadata": {},
   "source": [
    "We also user DBSCAN and apply hyperparameter tuning on epsilons and the minimum number of samples to find one with the best (highest) silhouette score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bf7207ca-babe-4109-989e-99936f286939",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tune_clusterer(df_pca):\n",
    "    best_score = -1\n",
    "\n",
    "    for eps in np.arange(0.1, 2, 0.1):\n",
    "        for min_samples in range(2, 3):\n",
    "            dbscan = DBSCAN(eps=eps, min_samples=min_samples)\n",
    "            labels = dbscan.fit_predict(df_pca)\n",
    "            n_clusters = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "            if n_clusters > 1:\n",
    "                score = silhouette_score(df_pca, labels)\n",
    "                if score > best_score:\n",
    "                    best_score = score\n",
    "                    best_eps = eps\n",
    "                    best_min_samples = min_samples\n",
    "\n",
    "    print(\"Best Silhouette Score:\", best_score)\n",
    "    print(\"Best eps:\", best_eps)\n",
    "    print(\"Best min_samples:\", best_min_samples)\n",
    "    \n",
    "    return best_eps, best_min_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "281ef3c1-eccc-496a-bd15-12bbaa784224",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Silhouette Score: 0.5545330937146584\n",
      "Best eps: 0.30000000000000004\n",
      "Best min_samples: 2\n",
      "CPU times: user 18.2 s, sys: 5.87 s, total: 24 s\n",
      "Wall time: 23.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "best_eps, best_min_samples = tune_clusterer(user_df_pca)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a0dc237f-2894-4c6d-9a41-c78b9f136876",
   "metadata": {},
   "source": [
    "Get cluster label using the tuned hyperparameters above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a1b52564-0e74-4830-9132-c44b57e6d4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_clusters(df_pca, best_eps, best_min_samples):\n",
    "    dbscan = DBSCAN(eps=best_eps, min_samples=best_min_samples)\n",
    "    labels = dbscan.fit_predict(df_pca)\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1dca5e5d-7644-47b4-a2bb-48cdc76513a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0,  1])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_df['user_clusters'] = generate_clusters(user_df_pca, best_eps, best_min_samples)\n",
    "np.unique(user_df['user_clusters'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b55b9d5c-85d5-4f6e-86a3-15731df6072f",
   "metadata": {},
   "source": [
    "Evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e0d2a59a-39ee-43c8-a4c4-12d599815a54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score: 0.5545330937146584\n"
     ]
    }
   ],
   "source": [
    "print(f\"Silhouette Score: {silhouette_score(user_df_pca, user_df['user_clusters'])}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c6434969-7719-4b4d-93d3-94a7d54eabe6",
   "metadata": {},
   "source": [
    "Perform the same procedures for `business_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f4542dcc-a244-493a-af81-4f1e2a7770d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Silhouette Score: 0.11581093584722416\n",
      "Best eps: 1.9000000000000001\n",
      "Best min_samples: 2\n",
      "Silhouette Score: 0.11581093584722416\n",
      "CPU times: user 26.1 s, sys: 4.05 s, total: 30.2 s\n",
      "Wall time: 16.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "business_df_scaled = scale(business_df)\n",
    "business_df_pca = pca(business_df_scaled)\n",
    "best_eps, best_min_samples = tune_clusterer(business_df_pca)\n",
    "business_df['business_clusters'] = generate_clusters(business_df_pca, best_eps, best_min_samples)\n",
    "print(f\"Silhouette Score: {silhouette_score(business_df_pca, business_df['business_clusters'])}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4742bf88-caaa-4829-a74b-2c8218f3528d",
   "metadata": {},
   "source": [
    "#### Model Extension 2: Sentiment Labels"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a6c6e898-4fe5-4595-a7ac-938069ecf1a5",
   "metadata": {},
   "source": [
    "Like the first extension we added, we will now perform sentiment analysis on review text and use the sentiment labels as another feature into the NCF model. We thought about using transfer learning on BERT to fine tune our own sentiment analysis model; however, transfer learning requires labeled data (the ground truth sentiment labels), which we do not have. So instaed, we will be using a pre-trained model. Note that transformers are really slow, so we will be using the Python library named `TextBlob` which is faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9a606570-ab98-44b8-ade0-1a3f0dfef3ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sentiment(text):\n",
    "    blob = TextBlob(text)\n",
    "    return blob.sentiment.polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eedf0c30-2740-46a4-a57f-ecab4270ab9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.64 s, sys: 68.3 ms, total: 3.71 s\n",
      "Wall time: 3.77 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "review_df['review_sentiment'] = review_text.apply(get_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3f599089-8c43-42c2-b04a-cc6e03df25bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.535714\n",
       "1    0.159977\n",
       "2    0.733333\n",
       "3   -0.551515\n",
       "4    0.097064\n",
       "Name: review_sentiment, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_df['review_sentiment'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c207d7c3-c4f6-4167-8caf-a23c3136c0ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11946.000000\n",
       "mean         0.260394\n",
       "std          0.230792\n",
       "min         -1.000000\n",
       "25%          0.130695\n",
       "50%          0.264103\n",
       "75%          0.398842\n",
       "max          1.000000\n",
       "Name: review_sentiment, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_df['review_sentiment'].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "daedaa41-6ad8-480b-9fbe-3938176ed06d",
   "metadata": {},
   "source": [
    "#### NCF Model with the Two Extensions: Cluster and Sentiment Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0a715b1d-f79e-47c7-b802-4ef76adbd988",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(review_df, user_df, on='user_id')\n",
    "df = pd.merge(df, business_df, on='business_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "08864f35-1534-447a-8347-9769f9eee413",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3aa2844f-ca8c-4508-9402-7488c5f88a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = feature_scaling(df_train, df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b2e35824-18d6-4c0c-ad14-2676da4addff",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model(df=df, user_df=user_df, business_df=business_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dd81856b-c10a-40fa-94b0-c61b23773b29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 4.2128 - val_loss: 1.9148\n",
      "Epoch 2/15\n",
      "269/269 [==============================] - 1s 2ms/step - loss: 1.7431 - val_loss: 1.8390\n",
      "Epoch 3/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.6692 - val_loss: 1.7998\n",
      "Epoch 4/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.6097 - val_loss: 1.7651\n",
      "Epoch 5/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.5537 - val_loss: 1.7360\n",
      "Epoch 6/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.5043 - val_loss: 1.7086\n",
      "Epoch 7/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.4576 - val_loss: 1.6836\n",
      "Epoch 8/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.4108 - val_loss: 1.6561\n",
      "Epoch 9/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.3663 - val_loss: 1.6374\n",
      "Epoch 10/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.3186 - val_loss: 1.6051\n",
      "Epoch 11/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.2747 - val_loss: 1.5804\n",
      "Epoch 12/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.2314 - val_loss: 1.5585\n",
      "Epoch 13/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.1947 - val_loss: 1.5326\n",
      "Epoch 14/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.1537 - val_loss: 1.5125\n",
      "Epoch 15/15\n",
      "269/269 [==============================] - 1s 3ms/step - loss: 1.1194 - val_loss: 1.4951\n",
      "CPU times: user 24.8 s, sys: 8.61 s, total: 33.4 s\n",
      "Wall time: 12.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_model(model, df_train=df_train, user_df=user_df, business_df=business_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "903b4fa9-6352-4442-9c75-1c0b49c5a9b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75/75 [==============================] - 0s 708us/step - loss: 1.3760\n",
      "Test loss (MSE): 1.376\n"
     ]
    }
   ],
   "source": [
    "test_model(model, df_test=df_test, user_df=user_df, business_df=business_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4e162f5e-2683-4733-894c-d6328f8caa07",
   "metadata": {},
   "source": [
    "This NCF model with the extensions performs slightly better than the NCF model without the extensions (RMSE 1.173 versus 1.185)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dafb1ba7",
   "metadata": {},
   "source": [
    "## Conclusion and Future Work\n",
    "\n",
    "In this project, we explore a neural collaborative filtering (NCF) approach to build a restaurant recommender system. We create two NCF models, one with and another without the extended cluster and sentiment labels. Both incorporate additional features associated with users and restaurants. We compare our two models to a baseline model using a standard user-based collaborative filtering approach. The results show that the NCF models lead to an over 17% decrease in the test root mean squared error. (The full test results are shown in Table 1 below.) This is likely because, unlike the standard approach which suffers from sparse data, the neural network architecture can incorporate additional features on users and items and is able to learn individual preferences among the users.\n",
    "\n",
    "However, there are multiple areas of possible improvement. First, an extensive finetuning process on the NCF network can be further made. Additionally, we would like to finetune and explore other approaches for clustering and sentiment analysis tasks, such as KMeans clustering. Moreover, this work has been focused solely on collaborative filtering, and in the future, we would like to compare our NCF models with the content-based, or hybrid, filtering approaches. \n",
    "\n",
    "**Table 1**:\n",
    "| Model | Test RMSE |\n",
    "|:----:|:-----:|\n",
    "| Baseline | 1.409 |\n",
    "| NCF | 1.185 |\n",
    "| NCF with Extended Fetures | 1.173 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "41668b24-b830-4152-bd1f-27bb9c37a07e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed running notebook: 0:07:20.770069\n"
     ]
    }
   ],
   "source": [
    "t1 = datetime.now()\n",
    "print(f'Time elapsed running notebook: {t1 - t0}')"
   ]
  }
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