Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model

TL;DR

This paper introduces a probabilistic chain graph model that combines semi-supervised learning with latent factor models to improve recommendation accuracy, especially in sparse data scenarios.

Contribution

It proposes a novel chain graph model integrating Bayesian networks and Markov random fields for enhanced recommendation with semi-supervised learning.

Findings

Significantly outperforms state-of-the-art methods.

Achieves larger improvements with increased data sparsity.

Demonstrates effective label propagation in recommender systems.

Abstract

Recently latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the sparsity of the rating matrix always limits their performance. Meanwhile, semi-supervised learning (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the smoothness insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this paper, we propose a novel probabilistic chain graph model (CGM) to marry SSL with LFM. The proposed CGM is a combination of Bayesian network and Markov random field. The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constraint between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.