research-article

GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction

Authors:

Fei WangAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2120 - 2130

https://doi.org/10.1145/3485447.3512085

Published: 25 April 2022 Publication History

Abstract

Users’ social connections have recently shown significant benefits to session-based recommendations, and graph neural networks have demonstrated great success in learning the pattern of information flow among users. However, the current paradigm presumes a given social network, which is not necessarily consistent with the fast-evolving shared interests and is expensive to collect. We propose a novel idea to learn the graph structure among users and make recommendations collectively in a coupled framework. This idea raises two challenges, i.e., scalability and effectiveness. We introduce a novel graph-structure learning framework for session-based recommendations (GSL4Rec) for solving both challenges simultaneously. Our framework has a two-stage strategy, i.e., the coarse neighbor screening and the self-adaptive graph structure learning, to enable the exploration of potential links among all users while maintaining a tractable amount of computation for scalability. We also propose a phased heuristic learning strategy to sequentially and synergistically train the graph learning part and recommendation part of GSL4Rec, thus improving the effectiveness by making the model easier to achieve good local optima. Experiments on five public datasets demonstrate that our proposed model significantly outperforms strong baselines, including state-of-the-art social network-based methods.

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Cited By

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
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
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
Show More Cited By

Index Terms

GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

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Cited By

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
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
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
Wei CFan YZhang JJia ZYan R(2024)Dynamic Relation Graph Learning for Time-Aware Service RecommendationIEEE Transactions on Network and Service Management10.1109/TNSM.2023.332597721:2(1503-1517)Online publication date: Apr-2024
https://doi.org/10.1109/TNSM.2023.3325977
Zhang XXu BMa FLi CLin YLin H(2023)Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based RecommendationACM Transactions on Information Systems10.1145/363194042:3(1-28)Online publication date: 29-Dec-2023
https://dl.acm.org/doi/10.1145/3631940
Yin MWang HXu XWu LZhao SGuo WLiu YTang RLian DChen EFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614781(3009-3019)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614781
Xiao XZhou WWen J(2023)LSIANInformation Sciences: an International Journal10.1016/j.ins.2023.119138642:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119138
Hao QWang CXiao YLin H(2022)IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationshipsApplied Intelligence10.1007/s10489-022-04215-753:11(14668-14689)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s10489-022-04215-7

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