Abstract
Recently, contrastive learning has been widely used in the field of sequential recommendation to solve the data sparsity problem. CL4Rec augments data through simple random crop, mask, and reorder, while DuoRec proposes a model-level data augmentation method. However, these methods do not take into account the issue of noisy data in sequential recommendation, such as false clicks during browsing. The noise may lead to poor representations of learned sequences and negatively affect the augmented data. Current sequential recommendation methods tend to learn the user’s intention from their original sequences, but these methods have certain limitations as the user’s intention for the next interaction may change. Based on the above observations, we propose Noise-augmented Contrastive Learning for Sequential Recommendation (NCL4Rec). Our NCL4Rec proposes sequential noise probability-guided data augmentation. We introduce supervised noise recognition during training instead of obtaining it from original sequences. Moreover, we design positive and negative augmentations of the sequence and design unique noise loss function to train them. Through experiments, it is verified that our NCL4Rec consistently outperforms the current state-of-the-art models.
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Acknowledgement
This work is supported in part by National Natural Science Foundation of China (61702264), the Open Research Project of State Key Laboratory of Novel Software Technology (Nanjing University, No. KFKT2022B28), the National Key R &D Program of China (No. 2020YFB1805503) and the Postdoctoral Science Foundation of China (2019M651835). Dr. Xuyun Zhang is supported only by ARC DECRA Grant DE210101458. Key Technologies and Industrialization of Industrial Internet Terminal Threat Detection and Response System.
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He, K. et al. (2023). Noise-Augmented Contrastive Learning for Sequential Recommendation. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_43
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DOI: https://doi.org/10.1007/978-981-99-7254-8_43
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