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CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation

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Leyu LinAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3901 - 3913

https://doi.org/10.1145/3580305.3599798

Published: 04 August 2023 Publication History

Abstract

Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and perform recommendations accordingly. State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations. Though effective and performing well, the models based on contrastive learning require careful selection of data augmentation methods and pretext tasks, efficient negative sampling strategies, and massive hyper-parameters validation. In this paper, we propose an ultra-simple alternative for obtaining better user representations and improving sequential recommendation performance. Specifically, we present a simple yet effective Consistency T braining method for sequential Recommendation (CT4Rec) in which only two extra training objectives are utilized without any structural modifications and data augmentation. Experiments on three benchmark datasets and one large newly crawled industrial corpus demonstrate that our proposed method outperforms SOTA models by a large margin and with much less training time than these based on contrastive learning. Online evaluation on real-world content recommendation system also achieves 2.717% improvement on the click-through rate and 3.679% increase on the average click number per capita. Further exploration reveals that such a simple method has great potential for CTR prediction. Our code is available at https://github.com/ct4rec/CT4Rec.git.

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Cited By

Bai YWang HHe J(2024)Blin: A Multi-Task Sequence Recommendation Based on Bidirectional KL-Divergence and Linear AttentionMathematics10.3390/math1215239112:15(2391)Online publication date: 31-Jul-2024
https://doi.org/10.3390/math12152391
Jin JZhang ZLi ZGao XYang XXiao LJiang JSerra ESpezzano F(2024)Pareto-based Multi-Objective Recommender System with Forgetting CurveProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680080(4603-4611)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680080
Wang ZShen YZhang ZHe LLi YGu HZhang YSerra ESpezzano F(2024)Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Sample SelectionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679681(2493-2502)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679681
Show More Cited By

Index Terms

CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 ACM.

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Published: 04 August 2023

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

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Cited By

Bai YWang HHe J(2024)Blin: A Multi-Task Sequence Recommendation Based on Bidirectional KL-Divergence and Linear AttentionMathematics10.3390/math1215239112:15(2391)Online publication date: 31-Jul-2024
https://doi.org/10.3390/math12152391
Jin JZhang ZLi ZGao XYang XXiao LJiang JSerra ESpezzano F(2024)Pareto-based Multi-Objective Recommender System with Forgetting CurveProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680080(4603-4611)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680080
Wang ZShen YZhang ZHe LLi YGu HZhang YSerra ESpezzano F(2024)Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Sample SelectionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679681(2493-2502)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679681
Hwang JJu HKang SJang SYu HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Multi-Domain Sequential Recommendation via Domain Space LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657685(2134-2144)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657685

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