research-article

Multi-Faceted Global Item Relation Learning for Session-Based Recommendation

Authors:

Li LiAuthors Info & Claims

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

Pages 1705 - 1715

https://doi.org/10.1145/3477495.3532024

Published: 07 July 2022 Publication History

Abstract

As an emerging paradigm, session-based recommendation is aimed at recommending the next item based on a set of anonymous sessions. Effectively representing a session that is normally a short interaction sequence renders a major technical challenge. In view of the limitations of pioneering studies that explore collaborative information from other sessions, in this paper we propose a new direction to enhance session representations by learning multi-faceted session-independent global item relations. In particular, we identify three types of advantageous global item relations, including negative relations that have not been studied before, and propose different graph construction methods to capture such relations. We then devise a novel multi-faceted global item relation (MGIR) model to encode different relations using different aggregation layers and generate enhanced session representations by fusing positive and negative relations. Our solution is flexible to accommodate new item relations and can easily integrate existing session representation learning methods to generate better representations from global relation enhanced session information. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over a large number of state-of-the-art methods. Specifically, we show that learning negative relations is critical for session-based recommendation.

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Choi MKim HCho HLee JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Multi-intent-aware Session-based RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657928(2532-2536)Online publication date: 11-Jul-2024
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Index Terms

Multi-Faceted Global Item Relation Learning for Session-Based Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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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
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J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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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

Guo LZhou STang HZheng XLuo Y(2025)Multi-Behavior Hypergraph Contrastive Learning for Session-Based RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352338337:3(1325-1338)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3523383
王益(2024)A Hypergraph-Enhanced Session Recommendation Model Based on Co-Occurrence Relationship ReconstructionModeling and Simulation10.12677/mos.2024.13323213:03(2543-2557)Online publication date: 2024
https://doi.org/10.12677/mos.2024.133232
Choi MKim HCho HLee JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Multi-intent-aware Session-based RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657928(2532-2536)Online publication date: 11-Jul-2024
https://doi.org/10.1145/3626772.3657928
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: 11-Jul-2024
https://doi.org/10.1145/3626772.3657748
Sun ZLiu HQu XFeng KWang YOng YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Large Language Models for Intent-Driven Session RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657688(324-334)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657688
Dai DTsai R(2024)Tri-directional Hypergraph Contrastive Learning for Session-based Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651241(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651241
Zhang CHan QChen RZhao XTang PSong H(2024)SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00067(803-815)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00067
Zhang ZZhang JChen JHuang YHuang X(2024)Self-Supervised Enhancement Method for Multi-Behavior Session-Based RecommendationIEEE Access10.1109/ACCESS.2024.350449612(175268-175277)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3504496
Zeng BChi JHong PLu GZhang DChen B(2024)Context-aware graph embedding with gate and attention for session-based recommendationNeurocomputing10.1016/j.neucom.2023.127221574:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127221
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
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