Movie Recommender System Based on Collaborative Filtering Using Apache Spark

Abstract

Recently, the building of recommender systems becomes a significant research area that attractive several scientists and researchers across the world. The recommender systems are used in a variety of areas including music, movies, books, news, search queries, and commercial products. Collaborative Filtering algorithm is one of the popular successful techniques of RS, which aims to find users closely similar to the active one in order to recommend items. Collaborative filtering (CF) with alternating least squares (ALS) algorithm is the most imperative techniques which are used for building a movie recommendation engine. The ALS algorithm is one of the models of matrix factorization related CF which is considered as the values in the item list of user matrix. As there is a need to perform analysis on the ALS algorithm by selecting different parameters which can eventually help in building efficient movie recommender engine. In this paper, we propose a movie recommender system based on ALS using Apache Spark. This research focuses on the selection of parameters of ALS algorithms that can affect the performance of a building robust RS. From the results, a conclusion is drawn according to the selection of parameters of ALS algorithms which can affect the performance of building of a movie recommender engine. The model evaluation is done using different metrics such as execution time, root mean squared error (RMSE) of rating prediction, and rank in which the best model was trained. Two best cases are chosen based on best parameters selection from experimental results which can lead to building good prediction rating for a movie recommender.