Movie Recommendation Systems Based on Collaborative Filtering: A Case Study on Netflix

Abstract

User ratings on items like movies, songs, and shopping products are used by Recommendation Systems (RS) to predict user preferences for items that have not been rated. RS has been utilized to give suggestions to users in various domains and one of the applications of RS is movie recommendation. In this domain, three general algorithms are applied; Collaborative Filtering that provides prediction based on similarities among users, Content-Based Filtering that is fed from the relation between item-user pairs and Hybrid Filtering one which combines these two algorithms. In this paper, we discuss which methods are more efficient in movie recommendation in the framework of Collaborative Filtering. In our analysis, we use Netflix Prize dataset and compare well-known Collaborative Filtering methods which are Singular Value Decomposition, Singular Value Decomposition++, KNearest Neighbour and Co-Clustering. The error of each method is calculated by using Root Mean Square Error (RMSE). Finally, we conclude that K-Nearest Neighbour method is more successful in our dataset.