research-article

CBML: A Cluster-based Meta-learning Model for Session-based Recommendation

Authors:

Chengfei LiuAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 1713 - 1722

https://doi.org/10.1145/3459637.3482239

Published: 30 October 2021 Publication History

Abstract

Session-based recommendation is to predict an anonymous user's next action based on the user's historical actions in the current session. However, the cold-start problem of limited number of actions at the beginning of an anonymous session makes it difficult to model the user's behavior, i.e., hard to capture the user's various and dynamic preferences within the session. This severely affects the accuracy of session-based recommendation. Although some existing meta-learning based approaches have alleviated the cold-start problem by borrowing preferences from other users, they are still weak in modeling the behavior of the current user. To tackle the challenge, we propose a novel cluster-based meta-learning model for session-based recommendation. Specially, we adopt a soft-clustering method and design a parameter gate to better transfer shared knowledge across similar sessions and preserve the characteristics of the session itself. Besides, we apply two self-attention blocks to capture the transition patterns of sessions in both item and feature aspects. Finally, comprehensive experiments are conducted on two real-world datasets and demonstrate the superior performance of CBML over existing approaches.

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https://dl.acm.org/doi/10.1007/s11280-024-01267-2
Zhu NSun LCao JLu XLi R(2023)Enhancing Next-Item Recommendation Through Adaptive User Group ModelingJournal of Social Computing10.23919/JSC.2023.00134:2(112-124)Online publication date: Jun-2023
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Index Terms

CBML: A Cluster-based Meta-learning Model 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

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Published: 30 October 2021

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National Natural Science Foundation of China
young scholar program of Cyrus Tang Foundation
the major project of natural science research in universities of Jiangsu province
Australian Research Council Discovery Projects
the priority academic program development of Jiangsu higher education institutions
Australian Research Council Linkage Project

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November 1 - 5, 2021

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Cited By

Zhang XXu BWu YZhong YLin HMa FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)FineRec: Exploring Fine-grained Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657761(1599-1608)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657761
Hong JYang WChao PFang J(2024)GroupMO: a memory-augmented meta-optimized model for group recommendationWorld Wide Web10.1007/s11280-024-01267-227:3Online publication date: 18-Apr-2024
https://dl.acm.org/doi/10.1007/s11280-024-01267-2
Zhu NSun LCao JLu XLi R(2023)Enhancing Next-Item Recommendation Through Adaptive User Group ModelingJournal of Social Computing10.23919/JSC.2023.00134:2(112-124)Online publication date: Jun-2023
https://doi.org/10.23919/JSC.2023.0013
Xiao YZhao SZhou ZHuan ZJu LZhang XWang LZhou JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615208(4365-4369)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615208
Li JZhang YLin XYang XZhou GLi LChen HZhou JChen HDuh WHuang HKato MMothe JPoblete B(2023)TAML: Time-Aware Meta Learning for Cold-Start Problem in News RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592068(2415-2419)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592068
Qin XYuan HZhao PFang JZhuang FLiu GLiu YSheng VChen HDuh WHuang HKato MMothe JPoblete B(2023)Meta-optimized Contrastive Learning for Sequential RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591727(89-98)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591727
Xu JZhang HWang XLv P(2023)AdaMLKnowledge-Based Systems10.1016/j.knosys.2023.110925279:COnline publication date: 4-Nov-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110925
Xu JWang XZhang HLv P(2023)Heterogeneous and clustering-enhanced personalized preference transfer for cross-domain recommendationInformation Fusion10.1016/j.inffus.2023.10189299:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.inffus.2023.101892
Chen JZhu TZha QWang Z(2023)Delayed evolutionary game clustering-based recommendation algorithm via latent information and user preferenceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106535124:COnline publication date: 1-Sep-2023
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Ren JZhang R(2023)When Alignment Makes a Difference: A Content-Based Variational Model for Cold-Start CTR PredictionAdvanced Data Mining and Applications10.1007/978-3-031-46661-8_48(724-739)Online publication date: 27-Aug-2023
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