research-article

An Attribute-Driven Mirror Graph Network for Session-based Recommendation

Authors:

Aixin SunAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1674 - 1683

https://doi.org/10.1145/3477495.3531935

Published: 07 July 2022 Publication History

Abstract

Session-based recommendation (SBR) aims to predict a user's next clicked item based on an anonymous yet short interaction sequence. Previous SBR models, which rely only on the limited short-term transition information without utilizing extra valuable knowledge, have suffered a lot from the problem of data sparsity. This paper proposes a novel mirror graph enhanced neural model for session-based recommendation (MGS), to exploit item attribute information over item embeddings for more accurate preference estimation.

Specifically, MGS utilizes two kinds of graphs to learn item representations. One is a session graph generated from the user interaction sequence describing users' preference based on transition patterns. Another is a mirror graph built by an attribute-aware module that selects the most attribute-representative information for each session item by integrating items' attribute information. We applied an iterative dual refinement mechanism to propagate information between the session and mirror graphs. To further guide the training process of the attribute-aware module, we also introduce a contrastive learning strategy that compares two mirror graphs generated for the same session by randomly sampling the attribute-same neighbors. Experiments on three real-world datasets exhibit that the performance of MGS surpasses many state-of-the-art models.

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Cited By

Sun JWang ZWu GWang HQiao BHan D(2025)Discreetly Exploiting Inter-Session Information for Session-Based RecommendationApplied Sciences10.3390/app1504215115:4(2151)Online publication date: 18-Feb-2025
https://doi.org/10.3390/app15042151
Boz AZorgdrager WKotti ZHarte JLouridas PKarakoidas VJannach DFragkoulis M(2025)Improving Sequential Recommendations with LLMsACM Transactions on Recommender Systems10.1145/3711667Online publication date: 10-Jan-2025
https://doi.org/10.1145/3711667
Jiang YGao YSun YWang SYan C(2024)Multi-Channel Hypergraph Collaborative Filtering with Attribute InferenceElectronics10.3390/electronics1305090313:5(903)Online publication date: 27-Feb-2024
https://doi.org/10.3390/electronics13050903
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Index Terms

An Attribute-Driven Mirror Graph Network for Session-based Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Abstract
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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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National Natural Science Foundation of China

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SIGIR '22

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SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Sun JWang ZWu GWang HQiao BHan D(2025)Discreetly Exploiting Inter-Session Information for Session-Based RecommendationApplied Sciences10.3390/app1504215115:4(2151)Online publication date: 18-Feb-2025
https://doi.org/10.3390/app15042151
Boz AZorgdrager WKotti ZHarte JLouridas PKarakoidas VJannach DFragkoulis M(2025)Improving Sequential Recommendations with LLMsACM Transactions on Recommender Systems10.1145/3711667Online publication date: 10-Jan-2025
https://doi.org/10.1145/3711667
Jiang YGao YSun YWang SYan C(2024)Multi-Channel Hypergraph Collaborative Filtering with Attribute InferenceElectronics10.3390/electronics1305090313:5(903)Online publication date: 27-Feb-2024
https://doi.org/10.3390/electronics13050903
Wang XDai TLiu QLiang SLarson K(2024)Spatial-temporal perceivingProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/267(2415-2423)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/267
Zhuo XQian SHu JDai FLin KWu G(2024)Multi-Hop Multi-View Memory Transformer for Session-Based RecommendationACM Transactions on Information Systems10.1145/366376042:6(1-28)Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1145/3663760
Zhang XXu BRen ZWang XLin HMa FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Disentangling ID and Modality Effects for Session-based RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657748(1883-1892)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657748
Li XTang YGuo CDing P(2024)Attribute-Enhanced Hypergraph Neural Networks for Session-based Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651027(1-7)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651027
Zhao XChen CSu JZhang YHu B(2024)Enhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00047(247-257)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00047
Gan MZhang XLiang Y(2024)CGG: Category-aware global graph contrastive learning for session-based recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112661305(112661)Online publication date: Dec-2024
https://doi.org/10.1016/j.knosys.2024.112661
Zhang LShen DKou YNie T(2024)Multi-perspective learning for enhanced user preferences for session-based recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111997298(111997)Online publication date: Aug-2024
https://doi.org/10.1016/j.knosys.2024.111997
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