Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract)

Jiewen Guan, Xin Huang, Bilian Chen*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


Social recommendation aims at improving recommendation performance by incorporating social information. Most existing social recommender systems only utilize the one-hop interpersonal social information, neglecting the community structure emerged in social networks, which may contain additional conducive information. In this paper, we propose a unified Simultaneous Community detection and Singular Value Decomposition (SCSVD) framework for community-aware social recommendation. An efficient optimization algorithm is also derived to optimize SCSVD, with an analysis of convergence and computational complexity. Comprehensive experimental results on three real-world benchmark datasets demonstrate the effectiveness of SCSVD, over both traditional matrix factorization based recommendation models and advanced neural network based recommendation models.
Original languageEnglish
Title of host publication2022 IEEE 38th International Conference on Data Engineering (ICDE)
Number of pages2
ISBN (Electronic)9781665408837
ISBN (Print)9781665408844
Publication statusPublished - 9 May 2022
Event38th IEEE International Conference on Data Engineering, ICDE 2022 - Virtual, Kuala Lumpur, Malaysia
Duration: 9 May 202212 May 2022

Publication series

NameInternational Conference on Data Engineering
ISSN (Print)1063-6382
ISSN (Electronic)2375-026X


Conference38th IEEE International Conference on Data Engineering, ICDE 2022
CityKuala Lumpur
Internet address


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