Scalable recommender system based on factor analysis

TL;DR

This paper introduces scalable statistical models, including extended crossed random effects and factor analysis, for recommender systems, utilizing EM algorithms to handle incomplete data and improve prediction accuracy.

Contribution

It extends crossed random effects models with random slopes and applies factor analysis with scalable EM algorithms to enhance recommender system performance.

Findings

Effective modeling of user-item interactions.

Scalable algorithms for incomplete data.

Improved prediction accuracy.

Abstract

Recommender systems have become crucial in the modern digital landscape, where personalized content, products, and services are essential for enhancing user experience. This paper explores statistical models for recommender systems, focusing on crossed random effects models and factor analysis. We extend the crossed random effects model to include random slopes, enabling the capture of varying covariate effects among users and items. Additionally, we investigate the use of factor analysis in recommender systems, particularly for settings with incomplete data. The paper also discusses scalable solutions using the Expectation Maximization (EM) and variational EM algorithms for parameter estimation, highlighting the application of these models to predict user-item interactions effectively.