research-article

Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation

Authors:

Enhong ChenAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 3297 - 3306

https://doi.org/10.1145/3589334.3645380

Published: 13 May 2024 Publication History

Abstract

In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to cart, and purchasing. Multi-behavior sequential recommendation aims to jointly consider multiple behaviors to improve the target behavior's performance. However, with diversified behavior data, user behavior sequences will become very long in the short term, which brings challenges to the efficiency of the sequence recommendation model. Meanwhile, some behavior data will also bring inevitable noise to the modeling of user interests. To address the aforementioned issues, firstly, we develop the Efficient Behavior Sequence Miner (EBM) that efficiently captures intricate patterns in user behavior while maintaining low time complexity and parameter count. Secondly, we design hard and soft denoising modules for different noise types and fully explore the relationship between behaviors and noise. Finally, we introduce a contrastive loss function along with a guided training strategy to contrast the valid information with the noisy signal in the data, and seamlessly integrate the two denoising processes to achieve a high degree of decoupling of the noisy signal. Sufficient experiments on real-world datasets demonstrate the effectiveness and efficiency of our approach in dealing with multi-behavior sequential recommendation.

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Cited By

Xie WZhou RWang HShen TChen ESerra ESpezzano F(2024)Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679756(2618-2628)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679756
Tong JYin MWang HPan QLian Dchen E(2024)MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain RecommendationWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_12(164-178)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-981-96-0570-5_12

Index Terms

Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

Copyright © 2024 ACM.

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Published: 13 May 2024

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WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

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Cited By

Xie WZhou RWang HShen TChen ESerra ESpezzano F(2024)Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679756(2618-2628)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679756
Tong JYin MWang HPan QLian Dchen E(2024)MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain RecommendationWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_12(164-178)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-981-96-0570-5_12

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