Movie Recommendation Engine using Collaborative Filtering with Alternative Least Square and Singular Value Decomposition Algorithms

Abstract: Recommender system is a process or approach used for filtering information from a vast dataset and predicting the choices to the users in the areas they are mostly interested in. This system nowadays is the backbone for the commercial aspect of an industry and is established in a variety of areas including movies, music, videos, web-pages, e-commerce, services etc. In this paper the focus is on movie recommendation and the technique currently present for this is the collaborative filtering technique. Of the collaborative filtering techniques, the matrix factorization algorithms namely Alternative Least Square and Singular Value Decomposition are implemented to predict or recommend the movies. Further to improve the processing and time computation for a large dataset we have used Apache Spark along with Elastic search and the accuracy is compared between the two algorithms for different values of testing subsets.

Keywords: Collaborative Filtering, ALS, SVD, Apache Spark, Elastic Search