Bayesian Additive Matrix Approximation for Social Recommendation
- 20 July 2021
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Knowledge Discovery From Data
- Vol. 16 (1), 1-34
- https://doi.org/10.1145/3451391
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.Keywords
Funding Information
- National Natural Science Foundation of China (61822601, 61773050, and 61632004)
- Beijing Natural Science Foundation (Z180006)
- National key research and development program (2017YFC1703506)
- Fundamental Research Funds for the Central Universities (2019JBZ110)
- Hong Kong Research Grants Council, General Research Fund (12306616, 12200317, 12300218, and 12300519)
- University of Hong Kong (104005583)
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