Collaborative Filtering with Social Local Models

TL;DR

This paper introduces SLOMA, a novel social-aware local low-rank matrix approximation method for recommendation systems, leveraging social connections to improve accuracy over traditional matrix factorization and LLORMA.

Contribution

It is the first to incorporate social connections into local low-rank matrix approximation for recommendations, enhancing performance with social regularization.

Findings

SLOMA outperforms LLORMA and MF on Yelp and Douban datasets.

Social regularization improves recommendation accuracy.

The proposed models demonstrate superior results in real-world tests.

Abstract

Matrix Factorization (MF) is a very popular method for recommendation systems. It assumes that the underneath rating matrix is low-rank. However, this assumption can be too restrictive to capture complex relationships and interactions among users and items. Recently, Local LOw-Rank Matrix Approximation (LLORMA) has been shown to be very successful in addressing this issue. It just assumes the rating matrix is composed of a number of low-rank submatrices constructed from subsets of similar users and items. Although LLORMA outperforms MF, how to construct such submatrices remains a big problem. Motivated by the availability of rich social connections in today's recommendation systems, we propose a novel framework, i.e., Social LOcal low-rank Matrix Approximation (SLOMA), to address this problem. To the best of our knowledge, SLOMA is the first work to incorporate social connections into the local low-rank framework. Furthermore, we enhance SLOMA by applying social regularization to submatrices factorization, denoted as SLOMA++. Therefore, the proposed model can benefit from both social recommendation and the local low-rank assumption. Experimental results from two real-world datasets, Yelp and Douban, demonstrate the superiority of the proposed models over LLORMA and MF.