Invite: Event Recommendation & Ticket Search System

A web recommendation system recommends nearby events according to users' favorite events, also allows users to search events and purchase tickets.

1. Project Description

• Frontend: an interactive web page with AJAX technology implemented with HTML, CSS, and JavaScript. This web application supports three key implementations:

• • Auto-Search events around users based on geolocation
  • Favorite events when the user likes and save them in the history records
  • Show recommendation of events around based on their favorite history.

• Backend: create Java servlet with RESTful APIs in Apache Tomcat to handle HTTP requests and responses

• • Utilized relational and NoSQL databases (MySQL/MongoDB) to store real business data, such as name, description, location, and price, from TicketMaster API

  • Design content-based recommendation algorithm for event recommendation

  • Deployed server to Amazon EC2 to handle 160 QPS tested by Apache JMeter

2. Architecture

• 3-tier architecture

  • Presentation tier: HTML, CSS, JavaScript
  • Data tier: MySQL, MongoDB
  • Logic tier: Java
  

3. Java Servlet 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

• TicketMasterAPI

• 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)

4. Database Implementation

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

• MongoDB

  • users : store user information and favorite history. (= users + history)

  • items : store item information and item-category relationship. ( = items + category)

  • logs : store log information

5. 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 functions like recommendations of event, restaurant, news, jobs, etc., Just link to different public API.
  • DBConnectionFactory.java: support multiple databases like MySQL and MongoDB. Improve extension ability.

• Singleton pattern: MySQLConnection.java, MongoDBConnection.java

  • Only create one specific instance of the database, and the class can control the instance itself and give global access to the outer class.