Bayesian Additive Matrix Approximation for Social Recommendation

Huafeng Liu, Liping Jing*, Jingxuan Wen, Pengyu Xu, Jian Yu, Michael K. Ng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

6 Citations (Scopus)

Abstract

Social relations between users have been proven to be a good type of auxiliary information to improve the recommendation performance. However, it is a challenging issue to sufficiently exploit the social relations and correctly determine the user preference from both social and rating information. In this article, we propose a unified Bayesian Additive Matrix Approximation model (BAMA), which takes advantage of rating preference and social network to provide high-quality recommendation. The basic idea of BAMA is to extract social influence from social networks, integrate them to Bayesian additive co-clustering for effectively determining the user clusters and item clusters, and provide an accurate rating prediction. In addition, an efficient algorithm with collapsed Gibbs Sampling is designed to inference the proposed model. A series of experiments were conducted on six real-world social datasets. The results demonstrate the superiority of the proposed BAMA by comparing with the state-of-The-Art methods from three views, all users, cold-start users, and users with few social relations. With the aid of social information, furthermore, BAMA has ability to provide the explainable recommendation.

Original languageEnglish
Article number7
Number of pages34
JournalACM Transactions on Knowledge Discovery from Data
Volume16
Issue number1
Early online date20 Jul 2021
DOIs
Publication statusPublished - Feb 2022

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

  • additive co-clustering
  • probabilistic graphical model
  • Recommendation system
  • social network

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