MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting

TL;DR

This paper introduces MovieMat, a context-aware movie recommendation system that uses matrix fitting for factorization, effectively incorporating contextual information like mood and weather, and outperforming classic methods in efficiency and fairness.

Contribution

The paper presents a novel matrix fitting approach within the MatMat framework for context-aware recommendations, addressing storage issues of tensor methods.

Findings

Outperforms classic matrix factorization in accuracy

Uses less storage than tensor-based methods

Achieves comparable fairness metrics

Abstract

Movie Recommender System is widely applied in commercial environments such as NetFlix and Tubi. Classic recommender models utilize technologies such as collaborative filtering, learning to rank, matrix factorization and deep learning models to achieve lower marketing expenses and higher revenues. However, audience of movies have different ratings of the same movie in different contexts. Important movie watching contexts include audience mood, location, weather, etc. Tobe able to take advantage of contextual information is of great benefit to recommender builders. However, popular techniques such as tensor factorization consumes an impractical amount of storage, which greatly reduces its feasibility in real world environment. In this paper, we take advantage of the MatMat framework, which factorizes matrices by matrix fitting to build a context-aware movie recommender system that is superior to classic matrix factorization and comparable in the fairness metric.