TY - JOUR
T1 - Community-aware Social Recommendation
T2 - A Unified SCSVD Framework
AU - Guan, Jiewen
AU - Huang, Xin
AU - Chen, Bilian
N1 - The work was supported in part by Youth Innovation Fund of Xiamen (Grant No. 3502Z20206049), National Natural Science Foundation of China (Grant No. 61836005) and HKRGC (Grant No. 22200320).
PY - 2023/3/1
Y1 - 2023/3/1
N2 - 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 N recommendation tasks, compared to fifteen state-of-the-art approaches.
AB - 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 N recommendation tasks, compared to fifteen state-of-the-art approaches.
KW - Social recommendation
KW - community detection
KW - optimization
KW - theoretical analysis
UR - http://www.scopus.com/inward/record.url?scp=85119619601&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2021.3117686
DO - 10.1109/TKDE.2021.3117686
M3 - Journal article
AN - SCOPUS:85119619601
SN - 1041-4347
VL - 35
SP - 2379
EP - 2393
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 3
ER -