research-article

Time Lag Aware Sequential Recommendation

Authors:

Philip S. YuAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 212 - 221

https://doi.org/10.1145/3511808.3557473

Published: 17 October 2022 Publication History

Abstract

Although a variety of methods have been proposed for sequential recommendation, it is still far from being well solved partly due to two challenges. First, the existing methods often lack the simultaneous consideration of the global stability and local fluctuation of user preference, which might degrade the learning of a user's current preference. Second, the existing methods often use a scalar based weighting schema to fuse the long-term and short-term preferences, which is too coarse to learn an expressive embedding of current preference. To address the two challenges, we propose a novel model called Time Lag aware Sequential Recommendation (TLSRec), which integrates a hierarchical modeling of user preference and a time lag sensitive fine-grained fusion of the long-term and short-term preferences. TLSRec employs a hierarchical self-attention network to learn users' preference at both global and local time scales, and a neural time gate to adaptively regulate the contributions of the long-term and short-term preferences for the learning of a user's current preference at the aspect level and based on the lag between the current time and the time of the last behavior of a user. The extensive experiments conducted on real datasets verify the effectiveness of TLSRec.

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Cited By

Li XWang NLiu XZeng JLi J(2025)Time-based Knowledge-aware framework for Multi-Behavior RecommendationExpert Systems with Applications10.1016/j.eswa.2025.126840273(126840)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126840
Yang XDong XYu JLi SXiong XShen H(2025)Locally enhanced denoising self-attention networks and decoupled position encoding for sequential recommendationComputers and Electrical Engineering10.1016/j.compeleceng.2025.110064123(110064)Online publication date: Apr-2025
https://doi.org/10.1016/j.compeleceng.2025.110064
Zhang RZhang HLuo YLiu ZCheng MLiu QChen ESerra ESpezzano F(2024)Learning the Dynamics in Sequential Recommendation by Exploiting Real-time InformationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679955(4288-4292)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679955
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Index Terms

Time Lag Aware Sequential Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Publication History

Published: 17 October 2022

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National Natural Science Foundation of China

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CIKM '22

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

GA, Atlanta, USA

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Li XWang NLiu XZeng JLi J(2025)Time-based Knowledge-aware framework for Multi-Behavior RecommendationExpert Systems with Applications10.1016/j.eswa.2025.126840273(126840)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126840
Yang XDong XYu JLi SXiong XShen H(2025)Locally enhanced denoising self-attention networks and decoupled position encoding for sequential recommendationComputers and Electrical Engineering10.1016/j.compeleceng.2025.110064123(110064)Online publication date: Apr-2025
https://doi.org/10.1016/j.compeleceng.2025.110064
Zhang RZhang HLuo YLiu ZCheng MLiu QChen ESerra ESpezzano F(2024)Learning the Dynamics in Sequential Recommendation by Exploiting Real-time InformationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679955(4288-4292)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679955
Yu YSugiyama KJatowt AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation with Collaborative Explanation via Mutual Information MaximizationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657770(1062-1072)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657770
Sun ZSi ZZhang XZang XSong YXu HXu JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes ProcessProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657732(1018-1028)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657732
Zheng XLi XChen ZSun LYu QGuo LLuo Y(2024)Enhanced Self-Attention Mechanism for Long and Short Term Sequential Recommendation ModelsIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33667718:3(2457-2466)Online publication date: Jun-2024
https://doi.org/10.1109/TETCI.2024.3366771
Su YZhu YSong MHe WHao G(2024)A Time-Aware Recommendation Model with Social Impact2024 11th International Conference on Dependable Systems and Their Applications (DSA)10.1109/DSA63982.2024.00035(199-208)Online publication date: 2-Nov-2024
https://doi.org/10.1109/DSA63982.2024.00035
Ryu HYu SYong Lee K(2024)Extending Transformer to Predict Both the Order and Occurrence Times of Elements in a Sequence2024 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp60711.2024.00074(371-372)Online publication date: 18-Feb-2024
https://doi.org/10.1109/BigComp60711.2024.00074
Feng YWen WHao ZCai R(2024)Time-aware tensor factorization for temporal recommendationApplied Intelligence10.1007/s10489-024-05851-x55:1Online publication date: 27-Nov-2024
https://doi.org/10.1007/s10489-024-05851-x
Chen RZhang YHu JWang XZhu JLiao W(2024)Behavior sessions and time-aware for multi-target sequential recommendationApplied Intelligence10.1007/s10489-024-05678-654:20(9830-9847)Online publication date: 23-Jul-2024
https://doi.org/10.1007/s10489-024-05678-6
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