Data-Centric Sequential Recommendation with Relation-Augmented Generation
Li, Yichen Tan, Yichen Shan, Yijing Wang, Haozhao Zhang, Rui Razzak, Imran Li, Ruixuan
Li, Yichen
Tan, Yichen
Shan, Yijing
Wang, Haozhao
Zhang, Rui
Razzak, Imran
Li, Ruixuan
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Department
Computational Biology
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Conference proceeding
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Abstract
Data-Centric Sequential Recommendation (DaCSR) has emerged as a promising technique that enhances dataset quality to better capture user preferences without increasing training complexity. However, mining item relations to improve data quality remains challenging due to the intricate nature of interaction sequences. Existing methods predominantly either: 1) optimize models to learn such item relations from fixed datasets at significant training cost, or 2) employ generative models to adaptively learn only interaction patterns, which lack interpretability and cannot guarantee effective data quality enhancement. In this paper, we pioneer a relation-guided dataset augmentation and regeneration framework for sequential recommendation called \textbf{RaSR}. This framework can significantly improve model performance on original datasets while maintaining training efficiency without modifying the model architecture. Specifically, we first preprocess user interactions to construct standardized sequential data and extract semantic representations via a Large Language Model (LLM). We then build a multi-relation graph with manually predefined metrics and semantic representations to generate augmented datasets. Finally, a relation-aware generator can produce regenerated datasets with both the multi-relation graph and the augmented dataset. To verify the effectiveness of RaSR, we conduct experiments on various backbone models and datasets, and achieve significant performance improvement compared to training the model only on the original dataset.
Citation
Y. Li, Y. Tan, Y. Shan, H. Wang, R. Zhang, I. Razzak , et al., "Data-Centric Sequential Recommendation with Relation-Augmented Generation," 2026, pp. 15162-15170.
Source
Proceedings of the AAAI Conference on Artificial Intelligence
Conference
AAAI Conference on Artificial Intelligence
Keywords
46 Information and Computing Sciences, 4605 Data Management and Data Science
Subjects
Source
AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence