research-article

Enhancing Multi-View Smoothness for Sequential Recommendation Models

Authors:

Wayne Xin ZhaoAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 4

Article No.: 107, Pages 1 - 27

https://doi.org/10.1145/3582495

Published: 08 April 2023 Publication History

Abstract

Sequential recommendation models aim to predict the interested items to a user based on his historical behaviors. To train sequential recommenders, implicit feedback data is widely adopted since it is easier to obtain than explicit feedback data. In the setting of implicit feedback, a user’s historical behaviors can be characterized as a chronologically ordered sequence of interacted items. From a perspective of machine learning, the historical interaction sequence and the recommended items can be considered as context and label, respectively, which are usually in one-hot representations in the recommendation models.

However, due to the discrete nature, one-hot representations are hard to sufficiently reflect the underlying user preference, and might also contain noise from implicit feedback that will mislead the model training. To solve these issues, we propose a general optimization framework, Multi-View Smoothness (MVS), to enhance the smoothness of sequential recommendation models in both data representations and model learning. Specifically, with the help of a complementary model, we smooth and enrich the one-hot representations of contexts and labels to better depict the underlying user preference (i.e., context smoothness and label smoothness), and devise a model regularization strategy to enforce the neighborhood smoothness of the model itself (i.e., model smoothness). Based on these strategies, we design three regularizers to constrain and improve the training of sequential recommendation models. Extensive experiments on five datasets show that our approach is able to improve the performance of various base models consistently and outperform other regularization training methods.

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Li NWang JLiu XWang LZhang L(2024)Contrastive Learning-based Meta-Learning Sequential Recommendation2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)10.1109/ICDCOT61034.2024.10515699(1-5)Online publication date: 15-Mar-2024
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Index Terms

Enhancing Multi-View Smoothness for Sequential Recommendation Models
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 4

October 2023

958 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3587261

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 08 April 2023

Online AM: 31 January 2023

Accepted: 05 January 2023

Revised: 26 October 2022

Received: 30 April 2022

Published in TOIS Volume 41, Issue 4

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National Natural Science Foundation of China
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Cited By

Luo TLiu YPan S(2024)Collaborative Sequential Recommendations via Multi-view GNN-transformersACM Transactions on Information Systems10.1145/364943642:6(1-27)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3649436
Li NWang JLiu XWang LZhang L(2024)Contrastive Learning-based Meta-Learning Sequential Recommendation2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)10.1109/ICDCOT61034.2024.10515699(1-5)Online publication date: 15-Mar-2024
https://doi.org/10.1109/ICDCOT61034.2024.10515699
Zhang ZYang BChen XLi Q(2024)A global contextual enhanced structural-aware transformer for sequential recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112515304(112515)Online publication date: Nov-2024
https://doi.org/10.1016/j.knosys.2024.112515
Liu SYang ZLiu SLiang RSun JLi QShen X(2024)Hyperbolic embedding of discrete evolution graphs for intelligent tutoring systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122451241:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122451
Zheng BHou YZhao WSong YZhu H(2023)Reciprocal Sequential RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608798(89-100)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608798

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