Performance Comparison of Algorithms for Movie Rating Estimation

TL;DR

This paper compares three algorithms for movie rating prediction using user-movie rating data, finding minimal performance differences and highlighting the effectiveness of iterative matrix factorization.

Contribution

It provides a comparative analysis of collaborative filtering, matrix factorization, and integrated models for movie rating prediction, emphasizing the performance of iterative matrix factorization.

Findings

No significant performance differences among algorithms

Iterative matrix factorization performs well despite simplicity

Complexity increase does not significantly improve accuracy

Abstract

In this paper, our goal is to compare performances of three different algorithms to predict the ratings that will be given to movies by potential users where we are given a user-movie rating matrix based on the past observations. To this end, we evaluate User-Based Collaborative Filtering, Iterative Matrix Factorization and Yehuda Koren's Integrated model using neighborhood and factorization where we use root mean square error (RMSE) as the performance evaluation metric. In short, we do not observe significant differences between performances, especially when the complexity increase is considered. We can conclude that Iterative Matrix Factorization performs fairly well despite its simplicity.