Learning with linear mixed model for group recommendation systems

TL;DR

This paper investigates the use of linear mixed models for group recommendation systems, effectively combining item attributes and user characteristics to improve response prediction accuracy.

Contribution

It introduces a linear mixed model approach for group recommendations, leveraging restricted maximum likelihood to model fixed and random effects.

Findings

Effective for group/user class recommendations

Collaborates item attributes with user characteristics

Demonstrates accuracy and speed on benchmark data

Abstract

Accurate prediction of users' responses to items is one of the main aims of many computational advising applications. Examples include recommending movies, news articles, songs, jobs, clothes, books and so forth. Accurate prediction of inactive users' responses still remains a challenging problem for many applications. In this paper, we explore the linear mixed model in recommendation system. The recommendation process is naturally modelled as the mixed process between objective effects (fixed effects) and subjective effects (random effects). The latent association between the subjective effects and the users' responses can be mined through the restricted maximum likelihood method. It turns out the linear mixed models can collaborate items' attributes and users' characteristics naturally and effectively. While this model cannot produce the most precisely individual level personalized recommendation, it is relative fast and accurate for group (users)/class (items) recommendation. Numerical examples on GroupLens benchmark problems are presented to show the effectiveness of this method.