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AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation

Authors:

Sunghun KimAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 976 - 986

https://doi.org/10.1145/3583780.3614773

Published: 21 October 2023 Publication History

Abstract

Sequential recommendation (SR) aims to model users' dynamic preferences from a series of interactions. A pivotal challenge in user modeling for SR lies in the inherent variability of user preferences. An effective SR model is expected to capture both the long-term and short-term preferences exhibited by users, wherein the former can offer a comprehensive understanding of stable interests that impact the latter. To more effectively capture such information, we incorporate locality inductive bias into the Transformer by amalgamating its global attention mechanism with a local convolutional filter, and adaptively ascertain the mixing importance on a personalized basis through layer-aware adaptive mixture units, termed as AdaMCT. Moreover, as users may repeatedly browse potential purchases, it is expected to consider multiple relevant items concurrently in long-/short-term preferences modeling. Given that softmax-based attention may promote unimodal activation, we propose the Squeeze-Excitation Attention (with sigmoid activation) into SR models to capture multiple pertinent items (keys) simultaneously. Extensive experiments on three widely employed benchmarks substantiate the effectiveness and efficiency of our proposed approach. Source code is available at https://github.com/juyongjiang/AdaMCT.

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Index Terms

AdaMCT: Adaptive Mixture of CNN-Transformer for 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 '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

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https://doi.org/10.1016/j.ipm.2024.103997
Niu YXing XJia ZLiu RXin M(2025)Implicit local–global feature extraction for diffusion sequence recommendationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109471139(109471)Online publication date: Jan-2025
https://doi.org/10.1016/j.engappai.2024.109471
Pratyasha PGupta S(2025)A computational intelligence-based approach for detecting drowsiness in sleep apnea patients using multimodal bio-signalsIntelligent Computing Techniques in Biomedical Imaging10.1016/B978-0-443-15999-2.00010-4(185-199)Online publication date: 2025
https://doi.org/10.1016/B978-0-443-15999-2.00010-4
Tang HWu SSun XZeng JXu GLi Q(2024)TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic RecommendationACM Transactions on Information Systems10.1145/368747043:1(1-27)Online publication date: 27-Aug-2024
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S AR R(2024)Enhancement in Context-Aware Recommender Systems – A Systematic Review2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493725(1-13)Online publication date: 22-Feb-2024
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