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Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation

Authors:

Huan LiuAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 357 - 366

https://doi.org/10.1145/3269206.3271725

Published: 17 October 2018 Publication History

Abstract

The explicitly observed social relations from online social platforms have been widely incorporated into recommender systems to mitigate the data sparsity issue. However, the direct usage of explicit social relations may lead to an inferior performance due to the unreliability (e.g., noises) of observed links. To this end, the discovery of reliable relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to adaptively identify implicit friends toward discovering more credible user relations. Particularly, implicit friends are those who share similar tastes but could be distant from each other on the network topology of social relations. Methodologically, to find the implicit friends for each user, we first model the whole system as a heterogeneous information network, and then capture the similarity of users through the meta-path based embedding representation learning. Finally, based on the intuition that social relations have varying degrees of impact on different users, our approach adaptively incorporates different numbers of similar users as implicit friends for each user to alleviate the adverse impact of unreliable social relations for a more effective recommendation. Experimental analysis on three real-world datasets demonstrates the superiority of our method and explain why implicit friends are helpful in improving social recommendation.

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Cited By

Fu HQin ZXue WDing G(2025)Fusing temporal and semantic dependencies for session-based recommendationInformation Processing & Management10.1016/j.ipm.2024.10389662:1(103896)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103896
Li MLiu KLiu HZhao ZWard TWu X(2024)Heterogeneous Meta-Path Graph Learning for Higher-Order Social RecommendationACM Transactions on Knowledge Discovery from Data10.1145/367365818:8(1-25)Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1145/3673658
Liu SLan YDeng XYi LZhu CYang LPark JGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)TrustGo: Trust Mining and Multi-semantic Regularization in Social RecommendationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658021(888-896)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658021
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Index Terms

Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

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Published: 17 October 2018

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National Science Foundation
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October 22 - 26, 2018

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Cited By

Fu HQin ZXue WDing G(2025)Fusing temporal and semantic dependencies for session-based recommendationInformation Processing & Management10.1016/j.ipm.2024.10389662:1(103896)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103896
Li MLiu KLiu HZhao ZWard TWu X(2024)Heterogeneous Meta-Path Graph Learning for Higher-Order Social RecommendationACM Transactions on Knowledge Discovery from Data10.1145/367365818:8(1-25)Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1145/3673658
Liu SLan YDeng XYi LZhu CYang LPark JGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)TrustGo: Trust Mining and Multi-semantic Regularization in Social RecommendationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658021(888-896)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658021
Sun YSun ZDu YZhang JOng YBaeza-Yates RBonchi F(2024)Self-Supervised Denoising through Independent Cascade Graph Augmentation for Robust Social RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671958(2806-2817)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671958
Chen XLei PSheng YLiu YGong ZSerra ESpezzano F(2024)Social Influence Learning for Recommendation SystemsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679598(312-322)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679598
Ma WWang YZhu YWang ZJing MZhao XYu JTang FAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)MADM: A Model-agnostic Denoising Module for Graph-based Social RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635784(501-509)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635784
Jiang WGao XXu GChen TYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Challenging Low Homophily in Social RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645460(3476-3484)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645460
Xing LHuang YZhang QWu HMa HZhang X(2024)A Counterfactual Inference-Based Social Network User-Alignment AlgorithmIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340599911:5(6939-6952)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3405999
Yu JLi JHe YZhu KZhang SHu W(2024)Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651350(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651350
Qin WQin JWang T(2024)Fusion of Personalized Implicit Relations for Social RecommendationIEEE Access10.1109/ACCESS.2024.335975112(30123-30134)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3359751
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