research-article

Soft Contrastive Sequential Recommendation

Authors:

Xu ChenAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 6

Article No.: 154, Pages 1 - 28

https://doi.org/10.1145/3665325

Published: 19 August 2024 Publication History

Abstract

Contrastive learning has recently emerged as an effective strategy for improving the performance of sequential recommendation. However, traditional models commonly construct the contrastive loss by directly optimizing human-designed positive and negative samples, resulting in a model that is overly sensitive to heuristic rules. To address this limitation, we propose a novel soft contrastive framework for sequential recommendation in this article. Our main idea is to extend the point-wise contrast to a region-level comparison, where we aim to identify instances near the initially selected positive/negative samples that exhibit similar contrastive properties. This extension improves the model’s robustness to human heuristics. To achieve this objective, we introduce an adversarial contrastive loss that allows us to explore the sample regions more effectively. Specifically, we begin by considering the user behavior sequence as a holistic entity. We construct adversarial samples by introducing a continuous perturbation vector to the sequence representation. This perturbation vector adds variability to the sequence, enabling more flexible exploration of the sample regions. Moreover, we extend the aforementioned strategy by applying perturbations directly to the items within the sequence. This accounts for the sequential nature of the items. To capture these sequential relationships, we utilize a recurrent neural network to associate the perturbations, which introduces an inductive bias for more efficient exploration of adversarial samples. To demonstrate the effectiveness of our model, we conduct extensive experiments on five real-world datasets.

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Cited By

Zhao YChen RLai RHan QSong HChen L(2024)Denoising and Augmented Negative Sampling for Collaborative FilteringACM Transactions on Recommender Systems10.1145/3690656Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1145/3690656
Zhu RZhu W(2024)Sequential Recommendation Based on Attention Mechanism and Fourier Transform2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC)10.1109/AIoTC63215.2024.10748274(219-223)Online publication date: 13-Sep-2024
https://doi.org/10.1109/AIoTC63215.2024.10748274

Index Terms

Soft Contrastive Sequential Recommendation
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 42, Issue 6

November 2024

813 pages

EISSN:1558-2868

DOI:10.1145/3618085

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 19 August 2024

Online AM: 16 May 2024

Accepted: 05 May 2024

Revised: 12 March 2024

Received: 25 October 2023

Published in TOIS Volume 42, Issue 6

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Cited By

Zhao YChen RLai RHan QSong HChen L(2024)Denoising and Augmented Negative Sampling for Collaborative FilteringACM Transactions on Recommender Systems10.1145/3690656Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1145/3690656
Zhu RZhu W(2024)Sequential Recommendation Based on Attention Mechanism and Fourier Transform2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC)10.1109/AIoTC63215.2024.10748274(219-223)Online publication date: 13-Sep-2024
https://doi.org/10.1109/AIoTC63215.2024.10748274

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