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Dual Side Deep Context-aware Modulation for Social Recommendation

Authors:

Jiajun ChenAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 2524 - 2534

https://doi.org/10.1145/3442381.3449940

Published: 03 June 2021 Publication History

Abstract

Social recommendation is effective in improving the recommendation performance by leveraging social relations from online social networking platforms. Social relations among users provide friends’ information for modeling users’ interest in candidate items and help items expose to potential consumers (i.e., item attraction). However, there are two issues haven’t been well-studied: Firstly, for the user interests, existing methods typically aggregate friends’ information contextualized on the candidate item only, and this shallow context-aware aggregation makes them suffer from the limited friends’ information. Secondly, for the item attraction, if the item’s past consumers are the friends of or have a similar consumption habit to the targeted user, the item may be more attractive to the targeted user, but most existing methods neglect the relation enhanced context-aware item attraction.

To address the above issues, we proposed DICER (Dual sIde deepContext-awarEmodulation for socialRecommendation). Specifically, we first proposed a novel graph neural network to model the social relation and collaborative relation, and on top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends’ information and item attraction. Empirical results on two real-world datasets show the effectiveness of the proposed model and further experiments are conducted to help understand how the dual context-aware modulation works.

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Cited By

Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3661821
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
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cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
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Published: 03 June 2021

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Cited By

Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3661821
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
Luo LWang MLiu JHuang J(2024)Inhomogeneous Interest Modeling via Hypergraph Convolutional Networks for Social Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651102(1-9)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651102
Liu SXu YMa S(2024)Hypergraph-Enhanced Self-Supervised Robust Graph Learning for Social RecommendationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448250(5545-5549)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448250
Gan MWang CYi LGu H(2024)Exploiting dynamic social feedback for session-based recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10363261:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103632
Han LQin JXia B(2023)Enhanced Social Recommendation Method Integrating Rating Bias OffsetsElectronics10.3390/electronics1218392612:18(3926)Online publication date: 18-Sep-2023
https://doi.org/10.3390/electronics12183926
Liu NZhao J(2023)Deep recommendation system based on knowledge graph and review textJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23058445:5(7661-7673)Online publication date: 4-Nov-2023
https://dl.acm.org/doi/10.3233/JIFS-230584
Khan BWu JYang JMa X(2023)Heterogeneous Hypergraph Neural Network for Social Recommendation using Attention NetworkACM Transactions on Recommender Systems10.1145/3613964Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3613964
Prakash TJalan RSingh BOnoe N(2023)CR-SoRec: BERT driven Consistency Regularization for Social RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608844(883-889)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608844
Yang YZhang CSong XDong ZZhu HLi W(2023)Contextualized Knowledge Graph Embedding for Explainable Talent Training Course RecommendationACM Transactions on Information Systems10.1145/359702242:2(1-27)Online publication date: 27-Sep-2023
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