Semantic-Aware Data Augmentation for Sequential Recommendation

摘要

Sequential recommendation (SR) aims to model users' dynamic preferences from their historical interaction sequences to provide personalized recommendations. However, data sparsity remains a core bottleneck limiting the performance of sequential recommendation models. Existing mixup methods face two major challenges: 1) They cannot effectively address the data sparsity dilemma in long-tail scenarios. 2) It is difficult to maintain the Semantic structure of augmented samples during the random mixing process. To address these challenges, this study proposes the Semantic-Aware Data Augmentation (SADA) framework, which utilizes large language models (LLMs) to generate semantic embeddings. This framework allows for the fusion of both collaborative and semantic signals, alleviating the representation deficiency of long-tail items. Additionally, through semantic-guided mixup, the framework preserves semantic structure consistency at both the user and item levels, thereby avoiding semantic structure degradation caused by traditional random mixing. This approach is expected to significantly improve recommendation performance and generalization ability across multiple datasets and application scenarios. In a broader context, this research aims to drive the evolution of data augmentation in sequential recommendation from heuristic methods to a semantic-driven paradigm, helping to build more personalized, accurate, and socially valuable recommendation services.