Social Recommendation with Learning Personal and Social Latent Factors

Huafeng Liu, Liping Jing*, Jian Yu, Kwok Po Ng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

27 Citations (Scopus)


Due to leveraging social relationships between users as well as their past social behavior, social recommendation becomes a core component in recommendation systems. Most existing social recommendation methods only consider direct social relationships among users (e.g., explicit and observed social relations). Recently, researchers proved that indirect social relationships can be effective to improve the recommendation quality when users only have few social connections, because it can identify the user interesting group even though the users have no observed social connection. In the literature, separate two-stage methods are studied, but they cannot explicitly capture the natural relationship between indirect social relations and latent user/item factors. In this paper, the main contribution is to propose a new joint recommendation model taking advantage of the Indirect Social Relations detection and Matrix Factorization collaborative filtering on social network and rating behavior information, which is called as InSRMF. In our work, the user latent factors can simultaneously and seamlessly capture user's personal preferences and social group characteristics. To optimize the InSRMF model, we develop a parallel graph vertex programming algorithm for efficiently handling large scale social recommendation data. Experiments based on four real-world datasets (Ciao, Epinions, Douban and Yelp) are conducted to demonstrate the performance of the proposed model. The experimental results have shown that InSRMF has ability to mine the proper indirect social relations and improve the recommendation performance compared with the testing methods in the literature, especially on the users with few social neighbors, Near-cold-start Users, Pure-cold-start Users and Long-tail Items.

Original languageEnglish
Article number8939381
Pages (from-to)2956-2970
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number7
Early online date23 Dec 2019
Publication statusPublished - 1 Jul 2021

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Social recommendation
  • indirect social relations
  • latent factor
  • matrix factorization


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