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Background

Contextual bandit algorithm makes use of context and user information to make personalized recommendation from dynamically changings pool of candidates. It sequentially selects destinations for users and receives feedback to adjust its selection strategy, which both exploiting and exploring their preferences. We implemented the LinUCB algorithm proposed by Li(2010) to realize restaurant recommendation.

SETUP

1. Notice:

  1. Please keep of positive word array in "preprocessing/pca_model_training.py", "preprocessing/data_stimulation.py/", "main/featureExtraction.py" consistent

  2. Difference between main/app.py and main/main.py:

    A. The file main/main.pycontains major functions used for making restaurant recommendation. In the if __name__=="__main__"part, there is a small program that integrated all the major functions to make a simple interactive recommender system running in terminal. Each time, three recommendations will be made, and you have to choose one that you like by entering its restaurant id. This small program aims to provide you with an intuitive feeling of how the algorithm works, and it can also help you check whether you set up correctly without running the web services.

    B. The file main/app.py imports the functions in main/main.py file and make the restaurant recommendation program a public web service. If you run python app.py, you can access the service by sending request conformed to the format in [section Descriptions -point3 (Two main services)].

2. Configuration File:

  1. Please create one python file under main folder main/config.py with Yelp API information(Yelp Fusion API)
api_key = <your api key>
client_id = <your client id>
app_name = <the name of your app>
  1. Remove all current data files under main/model/(they are user matrices and will be generated again according to your training input)/ Create one if it doesn't exist

3. Database Setup:

  1. Create a database called UrbanConnector in MySQL

  2. Import file /database/database_setup.sql to create three tables:AllRecommendations,RecommendationsSevenDays,UserRating

CREATE TABLE IF NOT EXISTS `AllRecommendations`(
    `user_id` VARCHAR(30), -- eg: "123124123213" any string given by front end
    `restaurant_id` VARCHAR(30), -- eg: "brLV35q22JnxSekUm1Wt8A", from yelp 
    `recommendation_time` TIMESTAMP DEFAULT CURRENT_TIMESTAMP, -- eg: "2019-08-08 00:45:00", local time generated by python 
    `context` JSON, -- eg: vector length of 54
    `local_time` VARCHAR(30), -- eg: "2019-08-08T00:44:58-04:00", utc time with ISO format
    PRIMARY KEY (`user_id`, `restaurant_id`, `recommendation_time`)
);

-- one for storing recommendations made only in the past 7 days
CREATE TABLE IF NOT EXISTS `RecommendationsSevenDays`(
    `user_id` VARCHAR(30), -- eg: "123124123213" any string given by front end
    `restaurant_id` VARCHAR(30), -- eg: "brLV35q22JnxSekUm1Wt8A", from yelp 
    `recommendation_time` TIMESTAMP DEFAULT CURRENT_TIMESTAMP , -- eg: "2019-08-08 00:45:00", local time generated by python 
    FOREIGN KEY (user_id, restaurant_id, recommendation_time) REFERENCES AllRecommendations(user_id, restaurant_id, recommendation_time) ON DELETE CASCADE
);

-- one for storing user rating 
CREATE TABLE IF NOT EXISTS `UserRating`(
    `user_id` VARCHAR(30), 
    `restaurant_id` VARCHAR(30), 
    `recommendation_time` TIMESTAMP DEFAULT CURRENT_TIMESTAMP , -- eg: "2019-08-08 00:45:00", local time generated by python 
    `user_selection_time` TIMESTAMP DEFAULT CURRENT_TIMESTAMP, -- eg: CURRENT_TIMESTAMP 2019-08-08 00:45:00
    `reward` FLOAT, -- domain(1,0,-0.5)
    FOREIGN KEY (user_id, restaurant_id, recommendation_time) REFERENCES AllRecommendations(user_id, restaurant_id, recommendation_time) ON DELETE CASCADE
);

  1. Change database setting and connect to database:

    A. To change the database of the webservice, modify main/app.py

    B. If you want to run this individual file and test the program with terminal, then modify the corresponding part in main/main.py

  2. (Optional)Test if the database is set up correctly(all modifications to the database):

    1. Update the RecommendationsSevenDays table

      test: DELETE FROM RecommendationsSevenDays WHERE recommendation_time < (NOW() - INTERVAL 7 DAY)

    2. Query recommendations for this user in the past 7 days

    3. Make recommendations and update both AllRecommendations RecommendationsSevenDays query execution1 execution2

      #notice that context is in json format
INSERT INTO `AllRecommendations`(`user_id`, `restaurant_id`, `recommendation_time`,`context`,`local_time`) VALUES (1231241412,brLV35q22JnxSekUm1Wt8A,2019-08-08 00:45:00,[1370.3069959094935, 3.5, 156, 1, 0.3678531037837738, 0.0245118791941913, 0.2884336991225436, -0.05679204451798186, 0.6186320352542078, 0.04952941385891926, 0.9322101108964919, -0.08197835826599327, 0.172544659806304, -0.2907603034548553, 0.052044121638843165, 0.36213477381726544, 0.02664460900300173, -0.12316523118316319, -0.04440593093193514, -0.12558986115695092, -0.026363099056166482, 0.06656524824647395, 0.0036090332773570824, -0.048723771666322656, -0.021830542997385245, -0.016607904142542648, 0.0175328386382096, 0.010678038991785925, 0.00031283715293467003, -0.015936172591742823, -0.007663354654449245, 0.006364743050601441, -0.018076355316729666, 0.013047002112322082, 0.0031238381114750296, 0.0012586542667849707, -0.004989686603965165, 0.019779303407707022, -0.004699269738667344, -0.012336437534250358, 0.008833647420428102, -0.001540721732683503, 0.003366412234424986, 0.019760043150998323, 0.006335963334146817, 0.00010389827537397681, -0.001825597229205127, 0.005353118672087066, 0.00468141683544218, 0.007346067350638506, 0.007177251878558236, -0.0013478173909830557, 0.00952805237548515, -0.0061604682930519765],2019-08-08T00:44:58-04:00);

      INSERT INTO `RecommendationsSevenDays`(user_id, restaurant_id, recommendation_time) VALUES (1231241412,brLV35q22JnxSekUm1Wt8A,2019-08-08 00:45:00)

    4. Update user profile according to the response

      INSERT INTO UserRating(user_id,restaurant_id,recommendation_time,user_selection_time,reward) VALUES(1231241412,brLV35q22JnxSekUm1Wt8A,2019-08-08 00:45:00,CURRENT_TIMESTAMP,1.0)

4. Install Dependency:

To install the dependencies, run: pip install -r requirements.txt

5. Start the Program:

First, make sure you have started your MySQL database

To start the web services, run under main folder (A) python app.py( or (B)python main.py locally in your terminal without webservice)

6. Expected output:

A. The expected output for main/main.py is three restaurant recommendations

B. Please refer to [section Descriptions - bulletin 3.Two main services] for detailed explanation about the output format. Note that if you don't have customer data in the database, you cannot test the feedback part.

7. Demo Website (Optional):

  1. Under folder "/demo/", run npm install

  2. Change the database setting in the "/demo/demo.js" file

  3. Start your MySQL database and then the API service by running python app.py under folder "/main" ( Working with (A))

  4. Start the demo program by running nodemon demo.js unser the folder "/demo"

  5. Enter "localhost:3000" in you browser.

Descriptions

1. Preprocessing:

  1. get_restaurant_data.py: get around 50K restaurant records from Yelp API to train PCA model and for offline evaluation.

    output: /data/restaurants_information/...(12 files)

  2. pca_model_training.py: convert text data(restaurant info) to feature matrix and train PCA model to 54-dimension.

    output: /main/pca_model.sav

  3. offline_evaluation_data_simulation.py: generated synthetic data (food preference) to test the algorithm, but much of the part is similar to pca_model_training program.

    output: /data/simulated_arm_contexts.pyc

2. Main:

  1. yelDataCollection.py: make request to Yelp API and retrieve candidate restaurants information. (Using multithreading strategy to increase the speed): it will give you some restaurants that satisfy your setting

  2. featureExtraction.py: convert the text data to feature matrix.

  3. linUCB.py: main recommendation algorithm using contextual bandit algorithm you can run it directly to see results of an offline evaluation

  4. main.py: main functions to implement the recommendation algorithm, such as get and save matrices, make recommendations and update the result.

  5. app.py: web framework

3. Two main services:

  1. Get recommendations -> return three restaurants(sometimes less than 3 options because there are not enough restaurants)
@app.route('/getRecommendation:<user_profile>&<user_id>&<local_time>&<longitude>&<latitude>&<radius>&<price>', methods=['GET'])
def getRecommendation(user_profile, user_id, time, longitude, latitude, radius, price):
# user_profile: string, eg: 'senior'
# user_id: string, eg:'123124'
# local_time: string-UTC timezone and ISO format, eg: '2019-08-07T23:44:16-04:00' (strict format)
# longitude: string, eg: '-73.984345'
# latitude: string, eg: '40.693899'
# radius(meters): string, eg: '1000' -> later to int 
# price: string, eg: '1','2','3','4'   ->later to int

sample request:

"GET /getRecommendation:senior&1231241412&2019-08-07T23:44:16-04:00&-73.984345&40.693899&2000&1 HTTP/1.1" 200 -

sample response:

1. success: retrieve three restaurants(sometimes is there is not enough restaurants, maybe only 1 or 2)
Response {type: "cors", url: "http://localhost:8000/getRecommendation:senior&1231241412&2019-08-07T23:44:16-04:00&-73.984345&40.693899&2000&1", redirected: false, status: 200, ok: true, …}
bodyUsed: true
headers: Headers {}
ok: true
redirected: false
status: 200
statusText: "OK"
type: "cors"
url: "http://localhost:8000/getRecommendation:senior&1231241412&2019-08-07T23:44:16-04:00&-73.984345&40.693899&2000&1"
body:{
    "success": [
      {
        "categories": [
          {
            "alias": "sandwiches", 
            "title": "Sandwiches"
          }, 
          {
            "alias": "desserts", 
            "title": "Desserts"
          }
        ], 
        "coordinates": {
          "latitude": 40.7158, 
          "longitude": -73.99169
        }, 
        "display_phone": "(646) 504-8132", 
        "distance": 2502.9117980819724, 
        "id": "gHdejB1Mx2P5UjAgZ6BT2w", 
        "location": "35 Orchard St", 
        "name": "Cheeky Sandwiches", 
        "phone": "+16465048132", 
        "price": "$", 
        "rating": 4.5, 
        "recommendation_time": "2019-08-07 12:08:15", 
        "review_count": 1118
      }, 
      {
        "categories": [
          {
            "alias": "falafel", 
            "title": "Falafel"
          }, 
          {
            "alias": "egyptian", 
            "title": "Egyptian"
          }, 
          {
            "alias": "sandwiches", 
            "title": "Sandwiches"
          }
        ], 
        "coordinates": {
          "latitude": 40.670295715332, 
          "longitude": -73.9790954589844
        }, 
        "display_phone": "(718) 768-4961", 
        "distance": 2661.7532896052808, 
        "id": "QZcRSVUltJ6YreTeDx52lQ", 
        "location": "226 7th Ave", 
        "name": "Mr Falafel", 
        "phone": "+17187684961", 
        "price": "$", 
        "rating": 4.0, 
        "recommendation_time": "2019-08-07 12:08:15", 
        "review_count": 184
      }, 
      {
        "categories": [
          {
            "alias": "chinese", 
            "title": "Chinese"
          }, 
          {
            "alias": "hkcafe", 
            "title": "Hong Kong Style Cafe"
          }
        ], 
        "coordinates": {
          "latitude": 40.71725, 
          "longitude": -73.99254
        }, 
        "display_phone": "(212) 966-8269", 
        "distance": 2687.6984035164082, 
        "id": "agCuWjaUJ8xBO_PDDQRiGw", 
        "location": "85 Eldridge St Lower E", 
        "name": "S Wan Cafe \u6d0b\u7d2b\u8346", 
        "phone": "+12129668269", 
        "price": "$", 
        "rating": 4.5, 
        "recommendation_time": "2019-08-07 12:08:15", 
        "review_count": 117
      }
    ]
  }
  
2.error: because the distance or price restriction is too tight, none of the restaurants satisfy the requirements in Yelp API
Response {type: "cors", url: "http://localhost:8000/getRecommendation:senior&1231241412&2019-08-07T23:44:16-04:00&-73.984345&40.693899&1&1", redirected: false, status: 200, ok: true, …}
body: (...)
bodyUsed: true
headers: Headers {}
ok: true
redirected: false
status: 200
statusText: "OK"
type: "cors"
url:  "http://localhost:8000/getRecommendation:senior&1231241412&2019-08-07T23:44:16-04:00&-73.984345&40.693899&1&1"
body:{
"error": "please relax restrictions of radius or price prference"
}

3.error: because there are no qualified destinations after running the recommendation algorithm
Response {type: "cors", url:  "http://localhost:8000/getRecommendation:senior&1231241412&2019-08-07T23:44:16-04:00&-73.984345&40.693899&100&1", redirected: false, status: 200, ok: true, …}
body: (...)
bodyUsed: true
headers: Headers {}
ok: true
redirected: false
status: 200
statusText: "OK"
type: "cors"
url: "http://localhost:8000/getRecommendation:senior+1231241412+12:08+-73.984345+40.693899+1+1"
body:{
"error": "no qualified destinations"
}

4.otherwise error
  1. Send users' feedback about the recommended restaurants
@app.route('/feedback:<user_profile>&<user_id>&<local_time>&<restaurant_id>&<recommendation_time>&<reward>',methods=['GET'])
def feedback(user_profile, user_id, local_time, restaurant_id, recommendation_time, reward):
# user_profile: string, eg: 'senior'
# user_id: string, eg:'123124'
# local_time: string-UTC timezone and ISO format, eg: '2019-08-07T23:44:16-04:00' (strict format)
# restaurant_id: string, eg: 'B0R-buSLWRbGFWpmqk_WZQ'
# recommendation_time: string, eg: '2019-08-07 2023:44:19', but in the request it replace empty space with %
# reward: string, eg: '-0.1','0','1' -> later to float

sample request:

"GET /feedback:senior&1231241412&2019-08-07T23:48:57-04:00&B0R-buSLWRbGFWpmqk_WZQ&2019-08-07%2023:44:19&1 HTTP/1.1" 200 -

"GET /feedback:senior&1231241412&2019-08-07T23:48:58-04:00&B0R-buSLWRbGFWpmqk_WZQ&2019-08-07%2023:44:19&-0.1 HTTP/1.1" 200 -

sample response:

1.success 200
Response {type: "cors", url: "http://localhost:8000/feedback:senior&1231241412&2…00&lQ7H-COT5duZQQ0XqGFPDg&2019-08-08%2000:06:16&1", redirected: false, status: 200, ok: true, …}
body: ReadableStream
locked: false
__proto__: ReadableStream
bodyUsed: false
headers: Headers {}
ok: true
redirected: false
status: 200
statusText: "OK"
type: "cors"
url: "http://localhost:8000/feedback:senior&1231241412&2019-08-08T00:07:31-04:00&lQ7H-COT5duZQQ0XqGFPDg&2019-08-08%2000:06:16&1"
__proto__: Response

2.error  500 
 printed line: "Try to insert a record, but doesn't conform to the foreign key policy"

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