Data Augmentation for Sequential Recommendation: A Survey
<|reference_start|>Data Augmentation for Sequential Recommendation: A Survey: As an essential branch of recommender systems, sequential recommendation (SR) has received much attention due to its well-consistency with real-world situations. However, the widespread data sparsity issue limits the SR model's performance. Therefore, researchers have proposed many data augmentation (DA) methods to mitigate this phenomenon and have achieved impressive progress. In this survey, we provide a comprehensive review of DA methods for SR. We start by introducing the research background and motivation. Then, we categorize existing methodologies regarding their augmentation principles, objects, and purposes. Next, we present a comparative discussion of their advantages and disadvantages, followed by the exhibition and analysis of representative experimental results. Finally, we outline directions for future research and summarize this survey. We also maintain a repository with a paper list at \url{https://github.com/KingGugu/DA-CL-4Rec}.<|reference_end|>
@article{dang2024data,
    title={Data Augmentation for Sequential Recommendation: A Survey},
    author={Yizhou Dang, Enneng Yang, Yuting Liu, Guibing Guo, Linying Jiang,
  Jianzhe Zhao, Xingwei Wang},
    journal={arXiv preprint arXiv:2409.13545},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13545},
    primaryClass={cs.IR}
}