Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
arxiv(2024)
摘要
Sequential recommender systems (SRS) are designed to predict users' future
behaviors based on their historical interaction data. Recent research has
increasingly utilized contrastive learning (CL) to leverage unsupervised
signals to alleviate the data sparsity issue in SRS. In general, CL-based SRS
first augments the raw sequential interaction data by using data augmentation
strategies and employs a contrastive training scheme to enforce the
representations of those sequences from the same raw interaction data to be
similar. Despite the growing popularity of CL, data augmentation, as a basic
component of CL, has not received sufficient attention. This raises the
question: Is it possible to achieve superior recommendation results solely
through data augmentation? To answer this question, we benchmark eight widely
used data augmentation strategies, as well as state-of-the-art CL-based SRS
methods, on four real-world datasets under both warm- and cold-start settings.
Intriguingly, the conclusion drawn from our study is that, certain data
augmentation strategies can achieve similar or even superior performance
compared with some CL-based methods, demonstrating the potential to
significantly alleviate the data sparsity issue with fewer computational
overhead. We hope that our study can further inspire more fundamental studies
on the key functional components of complex CL techniques. Our processed
datasets and codes are available at https://github.com/AIM-SE/DA4Rec.
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