系统梳理序列推荐数据增强方法，助你快速掌握技术脉络。
Data Augmentation for Sequential Recommendation: A Survey
- 按增强原理/对象/目的分类现有数据增强方法
- 对比分析各类方法优劣并展示代表性实验结果
- 适合想了解序列推荐数据增强的科研与工程人员
作为推荐系统的重要分支，序列推荐（SR）因其与真实场景的高度一致性受到广泛关注。然而，数据稀疏问题严重制约了模型性能。为此，研究者提出了多种数据增强（DA）方法，取得了显著进展。本文对针对序列推荐的数据增强方法进行了全面综述：首先介绍研究背景与动机；接着根据增强原理、对象和目的对现有方法进行分类；然后对比分析各类方法的优缺点，并展示代表性实验结果；最后总结未来研究方向并完成综述。本文还维护了一个包含相关论文的仓库，网址为：https://github.com/KingGugu/DA-CL-4Rec。
原文摘要 · Abstract (English)
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}.
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