research-article

Community Preserving Social Recommendation with Cyclic Transfer Learning

Authors:

Hongshu ChenAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 3

Article No.: 67, Pages 1 - 36

https://doi.org/10.1145/3631115

Published: 29 December 2023 Publication History

Abstract

Transfer learning-based recommendation mitigates the sparsity of user-item interactions by introducing auxiliary domains. Social influence extracted from direct connections between users typically serves as an auxiliary domain to improve prediction performance. However, direct social connections also face severe data sparsity problems that limit model performance. In contrast, users’ dependency on communities is another valuable social information that has not yet received sufficient attention. Although studies have incorporated community information into recommendation by aggregating users’ preferences within the same community, they seldom capture the structural discrepancies among communities and the influence of structural discrepancies on users’ preferences. To address these challenges, we propose a community-preserving recommendation framework with cyclic transfer learning, incorporating heterogeneous community influence into the rating domain. We analyze the characteristics of the community domain and its inter-influence on the rating domain, and construct link constraints and preference constraints in the community domain. The shared vectors that bridge the rating domain and the community domain are allowed to be more consistent with the characteristics of both domains. Extensive experiments are conducted on four real-world datasets. The results manifest the excellent performance of our approach in capturing real users’ preferences compared with other state-of-the-art methods.

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Cited By

Ayadi MMezni HElmannai HAlkanhel R(2025)Privacy-preserving cross-network service recommendation via federated learning of unified user representationsData & Knowledge Engineering10.1016/j.datak.2025.102422158(102422)Online publication date: Jul-2025
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https://doi.org/10.3389/fphy.2024.1457287
Liu MGuo QLiu J(2024)Effect of network structure on the accuracy of resilience dimension reductionFrontiers in Physics10.3389/fphy.2024.142055612Online publication date: 19-Jun-2024
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Index Terms

Community Preserving Social Recommendation with Cyclic Transfer Learning
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 3

May 2024

721 pages

EISSN:1558-2868

DOI:10.1145/3618081

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 29 December 2023

Online AM: 28 October 2023

Accepted: 16 October 2023

Revised: 28 August 2023

Received: 07 December 2022

Published in TOIS Volume 42, Issue 3

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Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
National Key R&D Program of China
Beijing Nova Program from Beijing Municipal Science & Technology Commission

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Cited By

Ayadi MMezni HElmannai HAlkanhel R(2025)Privacy-preserving cross-network service recommendation via federated learning of unified user representationsData & Knowledge Engineering10.1016/j.datak.2025.102422158(102422)Online publication date: Jul-2025
https://doi.org/10.1016/j.datak.2025.102422
Zhang LJian XMa Y(2024)Analysis of differences in fossil fuel consumption in the world based on the fractal time series and complex networkFrontiers in Physics10.3389/fphy.2024.145728712Online publication date: 2-Oct-2024
https://doi.org/10.3389/fphy.2024.1457287
Liu MGuo QLiu J(2024)Effect of network structure on the accuracy of resilience dimension reductionFrontiers in Physics10.3389/fphy.2024.142055612Online publication date: 19-Jun-2024
https://doi.org/10.3389/fphy.2024.1420556
Hao JMa JLiu STian Y(2024)A novel spreading dynamic based on adoption against the trendFrontiers in Physics10.3389/fphy.2024.140640312Online publication date: 12-Jun-2024
https://doi.org/10.3389/fphy.2024.1406403
Lin HZhang Z(2024)Modeling the Social Influence Effectiveness of Popular Weibo EventsComplexity10.1155/2024/64571152024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/6457115
Duan YTu YLu YWang X(2024)Improving graph collaborative filtering with view explorer for social recommendationJournal of Intelligent Information Systems10.1007/s10844-024-00865-w62:6(1703-1724)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10844-024-00865-w

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