Intent-Enhanced Data Augmentation for Sequential Recommendation

• URL: http://arxiv.org/abs/2410.08583v1
• Date: Fri, 11 Oct 2024 07:23:45 GMT
• Title: Intent-Enhanced Data Augmentation for Sequential Recommendation
• Authors: Shuai Chen, Zhoujun Li,
• Abstract summary: We propose an intent-enhanced data augmentation method for sequential recommendation(textbfIESRec) IESRec constructs positive and negative samples based on user behavior sequences through intent-segment insertion. The generated positive and negative samples are used to build a contrastive loss function, enhancing recommendation performance through self-supervised training.
• Score: 20.639934432829325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The research on intent-enhanced sequential recommendation algorithms focuses on how to better mine dynamic user intent based on user behavior data for sequential recommendation tasks. Various data augmentation methods are widely applied in current sequential recommendation algorithms, effectively enhancing the ability to capture user intent. However, these widely used data augmentation methods often rely on a large amount of random sampling, which can introduce excessive noise into the training data, blur user intent, and thus negatively affect recommendation performance. Additionally, these methods have limited approaches to utilizing augmented data, failing to fully leverage the augmented samples. We propose an intent-enhanced data augmentation method for sequential recommendation(\textbf{IESRec}), which constructs positive and negative samples based on user behavior sequences through intent-segment insertion. On one hand, the generated positive samples are mixed with the original training data, and they are trained together to improve recommendation performance. On the other hand, the generated positive and negative samples are used to build a contrastive loss function, enhancing recommendation performance through self-supervised training. Finally, the main recommendation task is jointly trained with the contrastive learning loss minimization task. Experiments on three real-world datasets validate the effectiveness of our IESRec model.

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