Semi-supervised learning meets factorization: Learning to recommend with chain graph model

Chaochao Chen, Kevin Chen Chuan Chang, Qibing Li, Xiaolin Zheng

Research output: Contribution to journalArticlepeer-review


Recently, latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predictmissing 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 ismainly 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 article, 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.

Original languageEnglish (US)
Article numbera73
JournalACM Transactions on Knowledge Discovery from Data
Issue number6
StatePublished - Aug 2018


  • Chain graph model
  • Data sparsity
  • Latent factor model
  • Semi-supervised learning

ASJC Scopus subject areas

  • Computer Science(all)


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