1. Movie Recommendation System
Jon C. Hammer
04/30/2015

2. Outline Introduction Background Architecture Implementation
Lessons Learned
Conclusion

3. Introduction Goal: Requirements:
Build a complete movie recommendation system.
Requirements:
User can view personalized recommendations
User can input new ratings
Interactive

4. ALS Collaborative Filtering technique Uses Matrix Factorization
Alternative to user/item based recommendation
Uses Matrix Factorization
Split ratings matrix (u x m) into U (u x d) and M (d x m)
u = Number of users
m = Number of movies
d = Number of intrinsic dimensions (our choice)
Alternate between optimizing U and M
Gradient Descent
Least Squares method

5. Architecture Key components: Recommender Database Web server
Client application
Client Application
Web Server
Recommender
Database

6. Implementation Platforms Languages Servers hosted on AWS
T2.micro and M3.xlarge EC2 instances
Ubuntu LTS
Additional Software:
Hadoop
Mahout
MySQL
Android application
Languages
Python
Bash
Java

7. Recommender Dataset Mahout MovieLens
20 million ratings
27,000 movies
138,000 users
Mahout
Given ratings matrix, produces N recommendations per user
Uses ALS algorithm
Recommendations are used by web server
Recomputed when new ratings are provided by user

8. Database User Table Movie Table MySQL implementation
Login & customer information
Movie Table
Movie name, year, IMDB link, and poster
Most information already provided in the dataset
Posters scraped from IMDB a priori for client applications
MySQL implementation

9. Web Server
Interface between clients and recommendation engine / database
Written in Python
Twisted, Klein, MySQLdb modules
Communication via HTTP Get, HTTP Post, JSON
Returns most recent recommendations
Interactive database queries

10. Client Application Features Written in Java for Android Login system
Ability to create new accounts on the fly
View personalized recommendations
Search database for movies
Enter new ratings
Written in Java for Android

11. Client Application

12. Lessons Learned Operate with AWS Hadoop / Mahout General
Configuring & launching instances
Creating images
VPC & Security
Hadoop / Mahout
Installation & configuration
HDFS
General
Making / responding to web requests in both Java & Python
Website scraping

13. References
Zhou, Yunhong, et al. "Large-scale parallel collaborative filtering for the netflix prize." Algorithmic Aspects in Information and Management. Springer Berlin Heidelberg,
Mahout.
Hadoop.
MovieLens dataset.

14. Questions?