research-article

Modeling User Reviews through Bayesian Graph Attention Networks for Recommendation

Authors:

Wei LiAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 3

Article No.: 78, Pages 1 - 29

https://doi.org/10.1145/3570500

Published: 25 April 2023 Publication History

Abstract

Recommender systems relieve users from cognitive overloading by predicting preferred items for users. Due to the complexity of interactions between users and items, graph neural networks (GNN) use graph structures to effectively model user–item interactions. However, existing GNN approaches have the following limitations: (1) User reviews are not adequately modeled in graphs. Therefore, user preferences and item properties that are described in user reviews are lost for modeling users and items; and (2) GNNs assume deterministic relations between users and items, which lack the stochastic modeling to estimate the uncertainties in neighbor relations. To mitigate the limitations, we build tripartite graphs to model user reviews as nodes that connect with users and items. We estimate neighbor relations with stochastic variables and propose a Bayesian graph attention network (i.e., ContGraph) to accurately predict user ratings. ContGraph incorporates the prior knowledge of user preferences to regularize the posterior inference of attention weights. Our experimental results show that ContGraph significantly outperforms 13 state-of-the-art models and improves the best performing baseline (i.e., ANR) by 5.23% on 25 datasets in the five-core version. Moreover, we show that correctly modeling the semantics of user reviews in graphs can help express the semantics of users and items.

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Cited By

Anand VMaurya A(2024)A Survey on Recommender Systems Using Graph Neural NetworkACM Transactions on Information Systems10.1145/369478443:1(1-49)Online publication date: 26-Nov-2024
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Chen HLi CChen TChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Skewness-aware Boosting Regression Trees for Customer Contribution Prediction in Financial Precision MarketingCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3648346(461-470)Online publication date: 13-May-2024
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Zhao SYuan XWu RHu ZLiu HWang KHu YLv TFan CTong XHan JZheng YHao JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)The MMO Economist: AI Empowers Robust, Healthy, and Sustainable P2W MMO EconomiesCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3648344(443-452)Online publication date: 13-May-2024
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Index Terms

Modeling User Reviews through Bayesian Graph Attention Networks for Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. 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 41, Issue 3

July 2023

890 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3582880

Editor:
Min Zhang
Tsinghua University, China

Issue’s Table of Contents

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Publication History

Published: 25 April 2023

Online AM: 09 November 2022

Accepted: 28 October 2022

Revised: 12 September 2022

Received: 04 April 2022

Published in TOIS Volume 41, Issue 3

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Cited By

Anand VMaurya A(2024)A Survey on Recommender Systems Using Graph Neural NetworkACM Transactions on Information Systems10.1145/369478443:1(1-49)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3694784
Chen HLi CChen TChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Skewness-aware Boosting Regression Trees for Customer Contribution Prediction in Financial Precision MarketingCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3648346(461-470)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648346
Zhao SYuan XWu RHu ZLiu HWang KHu YLv TFan CTong XHan JZheng YHao JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)The MMO Economist: AI Empowers Robust, Healthy, and Sustainable P2W MMO EconomiesCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3648344(443-452)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648344
Sun PWang YZhang MWu CFang YZhu HFang YWang MChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption UncertaintyCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3648297(10-19)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648297
Yang PXiao YZheng WLiu YHsu C(2024)A graph attention network with contrastive learning for temporal review-based recommendationsApplied Soft Computing10.1016/j.asoc.2024.111652159(111652)Online publication date: Jul-2024
https://doi.org/10.1016/j.asoc.2024.111652
Hong YYe W(2023)Review-Enhanced Sequential Recommendation with Self-Attention and Graph Collaborative Features2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00190(1493-1499)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00190

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