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Applying User Based Collaborative Filtering to Develop an Efficacious Model for the Movie Recommendation System

Gatik Gola

Student, King's College, Taunton, UK

8-13

Vol: 8, Issue: 3, 2018

Receiving Date: 2018-04-23 Acceptance Date:

2018-07-03

Publication Date:

2018-07-10

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Abstract

Recommender systems have become ubiquitous in our lives. Yet, currently, they are far from optimal. In this project, we attempt to understand the different kinds of recommendation systems and compare their performance on the MovieLens dataset. We attempt to build a scalable model to perform this analysis. We start by preparing and comparing the various models on a smaller dataset of 100,000 ratings. Then we try to scale the algorithm so that it is able to handle 20 million ratings by using Apache Spark. We find that for the smaller dataset, using user-based collaborative filtering results in the Mean Squared Error on our dataset.

Keywords: movie recommendation system; User Based Collaborative Filtering; data scrapping

References

  1. A Survey of Collaborative Filtering Techiques; Su et al; https://www.hindawi.com/journals/aai/2009/421425/
  2. Google News Personalization: Scalable Online Collaborative Filtering; Das et al; https://www2007.org/papers/paper570.pdf
  3. Intro to Recommender Systems: Collaborative Filtering; http://blog.ethanrosenthal.com/2015/11/02/intro-to-collborative-filtering/
  4. Collaborative Filtering Recommender Systems; Stanford student project; http://cs229.stanford.edu/proj2014/Rahul%20Makhijani,%20Saleh%20Samaneh,%20Megh%20Mehta,%20Collaborative%20Filtering%20Recommender%20Systems.pdf
  5. MMDS Course slides; Jeffrey Ullman; http://inflolab.satnford.edu/ullman/mmds/ch9.pdf
  6. Recommending items to more than a billion people; Kabiljo et al; https://code.facebook.com/posts/8619993875667/recommending-items-to-more-than-a-billion-people/
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