COMPARATIVE ANALYSIS OF MOVIE RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERING IN SPARK ENGINE

Goutham Miryala

Abstract


A personalized recommendation system learns user specific profiles from user feedback, so it can deliver information tailored to each individual user’s interest. A system serving millions of users can learn a better user profile for a new user, or a user with little feedback, by borrowing information from other users using a regression classifier. Regression is a technique borrowed by machine learning from the field of statistics. This paper proposes to build a recommendation system for the users. This paper uses a machine learning algorithm, which is imported from the spark. Apache spark is a fast and general engine used for large scale data processing. The efficacy and efficiency of the Alternating Least Squares (ALS) algorithm is compared with Singular Value decomposition, K-Nearest Neighbor algorithm, and Normal predictor algorithm. The ALS algorithm is justified by theory and demonstrated on actual user data from Movie Lens.

Keywords: ALS, Apache Spark, regression classifier, recommendation systems


Full Text:

PDF

Refbacks

  • There are currently no refbacks.


© 2017 International Journal of Global Research in Computer Science (JGRCS)
Copyright Agreement & Authorship Responsibility