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Data Augmentation for Sequential Recommendation: A Survey

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arxiv 2409.13545 v1 pith:Y7PGSUDA submitted 2024-09-20 cs.IR

Yizhou Dang , Enneng Yang , Yuting Liu , Guibing Guo , Linying Jiang , Jianzhe Zhao , Xingwei Wang This is my paper · ORCID
classification cs.IR
keywords augmentationdatasurveymethodsrecommendationresearchsequentialachieved
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation

    cs.IR 2025-05 conditional novelty 6.0 of 10

    An LLM trained by reinforcement learning to interact over multiple turns with a preference-aware recommender model outperforms both traditional and LLM-based baselines on sequential recommendation benchmarks.

  2. Augmenting Sequential Recommendation with Balanced Relevance and Diversity

    cs.IR 2024-12 conditional novelty 4.0 of 10

    BASRec improves sequential recommendation by mixing original and edited sequences in representation space, plus cross-user mixing, reporting average gains up to 72% on GRU4Rec.

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