research-article

Equivariant Contrastive Learning for Sequential Recommendation

Authors:

Sunghun KimAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 129 - 140

https://doi.org/10.1145/3604915.3608786

Published: 14 September 2023 Publication History

Abstract

Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. However, due to the inherent properties of user behavior sequences, some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be sub-optimal. Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e.g., item substitution) and insensitive to mild augmentations (e.g., feature-level dropout masking). In detail, we use the conditional discriminator to capture differences in behavior due to item substitution, which encourages the user behavior encoder to be equivariant to invasive augmentations. Comprehensive experiments on four benchmark datasets show that the proposed ECL-SR framework achieves competitive performance compared to state-of-the-art SR models. The source code is available at https://github.com/Tokkiu/ECL.

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Cited By

Cui ZWu HHe BCheng JMa CSerra ESpezzano F(2024)Context Matters: Enhancing Sequential Recommendation with Context-aware Diffusion-based Contrastive LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679655(404-414)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679655
Zhou PHuang YXie YGao JWang SKim JKim SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645661(3854-3863)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645661
Huang BYu WXie RLuo JXiao JHuang J(2024)Lorentz equivariant model for knowledge-enhanced hyperbolic collaborative filteringKnowledge-Based Systems10.1016/j.knosys.2024.111590291:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111590
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Index Terms

Equivariant Contrastive Learning for Sequential Recommendation
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

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Published: 14 September 2023

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Cited By

Cui ZWu HHe BCheng JMa CSerra ESpezzano F(2024)Context Matters: Enhancing Sequential Recommendation with Context-aware Diffusion-based Contrastive LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679655(404-414)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679655
Zhou PHuang YXie YGao JWang SKim JKim SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645661(3854-3863)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645661
Huang BYu WXie RLuo JXiao JHuang J(2024)Lorentz equivariant model for knowledge-enhanced hyperbolic collaborative filteringKnowledge-Based Systems10.1016/j.knosys.2024.111590291:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111590
Chen SChen JWan S(2024)Efficient weighted sequential pattern miningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122703243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122703
Sang LHu YZhang YZhang Y(2024)Multi-view denoising contrastive learning for bundle recommendationApplied Intelligence10.1007/s10489-024-05825-z54:23(12332-12346)Online publication date: 1-Dec-2024
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Wang YChen LHu P(2023)A Sequential Recommendation Model Combining Contrastive Learning and Self-Attention2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA)10.1109/ICIPCA59209.2023.10257669(1369-1373)Online publication date: 11-Aug-2023
https://doi.org/10.1109/ICIPCA59209.2023.10257669

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