Data Augmentation for Sequential Recommendation: A Survey

作     者：Dang, Yizhou Yang, Enneng Liu, Yuting Guo, Guibing Jiang, Linying Zhao, Jianzhe Wang, Xingwei 

作者机构：Software College Northeastern University China School of Computer Science and Engineering Northeastern University China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Recommender systems 

摘      要：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 https://***/KingGugu/DA-CL-4Rec. © 2024, CC BY.