Community-aware Social Recommendation: A Unified SCSVD Framework

Jiewen Guan, Xin Huang, Bilian Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Recommender system provides personalized suggestions based on users' interests and social connections. However, most existing social recommendation models utilize social relationships in a direct manner, i.e., they only consider the user-user connections, neglecting the clustering nature of social networks. As social information recursively spreads in the social network, the community structure, which contains richer information in contrast to pure user-user relationships, would emerge. To dismiss these limitations, in this paper, we propose a unified recommendation framework named Simultaneous Community detection and Singular Value Decomposition (SCSVD), which utilizes the underlying community structure to regularize user latent preferences. We propose a well-designed iterative optimization algorithm to tackle social recommendation efficiently. In addition, we theoretically analyze the proposed algorithm in terms of convergence, time complexity, and also the unified process of community detection and user embedding learning. Extensive experiments are conducted on three benchmark real-world datasets of product reviews, demonstrating the effectiveness, robustness, and flexibility of SCSVD in both rating prediction and top-N recommendation tasks, compared to fifteen state-of-the-art approaches.

Original languageEnglish
JournalIEEE Transactions on Knowledge and Data Engineering
DOIs
Publication statusE-pub ahead of print - 6 Oct 2021

Scopus Subject Areas

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

User-Defined Keywords

  • Analytical models
  • community detection
  • Convergence
  • Optimization
  • optimization
  • Predictive models
  • Recommender systems
  • Social networking (online)
  • Social recommendation
  • Task analysis
  • theoretical analysis

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