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Event-Recommendation-Web-Service

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Event Recommendation Systems

Business Design

  • To design a personalization based event recommendation systems for event search.

General Instruction

  • Design a web service with RESTful APIs in Java to handle HTTP requests and responses
  • Frontend: an interactive web page with AJAX technology implemented with HTML, CSS and JavaScript. The Event Recommendation Website realizes three main functions:
    • Search events around users
    • Favorite events they like and also delete events they don’t like anymore
    • Get recommendation of events around based on their favorite history and distance to where events will be hold
  • Backend: use Java to process logic request, and some supports are as below:
    • Built with both relational database and NoSQL database (MySQL and MongoDB) to support data storage from users and items searched in TicketMaster API
    • Design content-based recommendation algorithm for event recommendation
  • Deploy website server on Amazon EC2: Event Recommendation System
  • Analyze website traffic both online and offline with ELK (ElasticSearch, Logstash and Kibana) and MapReduce in MongoDB

Infrastructure Design

  • 3-tier architecture
    • Presentation tier: HTML, CSS, JavaScript
    • Data tier: MySQL, MongoDB
    • Logic tier: Java
  • Local and remote development environment

local environment

Local development environment

remote environment

Remote development environment

API Design

  • Logic tier(Java Servlet to RPC)
    • Search
      • searchItems
      • Ticketmaster API
      • parse and clean data, saveItems
      • return response
    • History
      • get, set, delete favorite items
      • query database
      • return response
    • Recommendation
      • recommendItems
      • get favorite history
      • search similar events, sorting
      • return response
    • Login
      • GET: check if the session is logged in
      • POST: verify the user name and password, set session time and marked as logged in
      • query database to verify
      • return response
    • Logout
      • GET: invalid the session if exists and redirect to index.html
      • POST: the same as GET
      • return response
    • Register
      • Set a new user into users table/collection in database
      • return response

APIs design

APIs design in logic tier

  • TicketMasterAPI
    Official Doc - Discovery API
  • Recommendation Algorithms design
    • Content-based Recommendation: find categories from item profile from a user’s favorite, and recommend the similar items with same categories.
    • Present recommended items with ranking based on distance (geolocation of users)

recommendation algorithm

Process of recommend request

Database Design

  • MySQL
    • users - store user information.
    • items - store item information.
    • category - store item-category relationship
    • history - store user favorite history

mysql

MySQL database design

  • MongoDB
    • users - store user information and favorite history. = (users + history)
    • items - store item information and item-category relationship. = (items + category)
    • logs – store log information

Implementation Details

  • Design pattern
    • Builder pattern: Item.java
      • When convert events from TicketMasterAPI to java Items, use builder pattern to freely add fields.
    • Factory pattern: ExternalAPIFactory.java, DBConnectionFactory.java
      • ExternalAPIFactory.java: support multiple function like recommendation of event, restaurant, news, jobs… just link to different public API like TicketMasterAPI. Improve extension ability.
      • DBConnectionFactory.java: support multiple database like MySQL and MongoDB. Improve extension ability.
    • Singleton pattern: MySQLConnection.java, MongoDBConnection.java
      • Only create specific number of instance of database, and the class can control the instance itself, and give the global access to outerclass

User Behavior Analysis

  • Online (ElasticSearch, Logstash, Kibana)
    • Use Logstash to fetch log (in NoSQL-like form), then store data in ElasticSearch, finally use Kibana to analyze the data in ElasticSearch, getting some tables and graphs like APIs use, request status, geolocation of visitors, etc

ELK analysis

Remote development environment

  • Offline (MapReduce in MongoDB)
    • Copy-paste some logs from Tomcat server
    • Purify log data and store in MongoDB
    • Do mapreduce() in MongoDB
    • Get a list of timebucket-count in descending order of count, then find the peak time of website traffic