华南理工大学学报（自然科学版）2026，Vol.54Issue(8)：62-73,12.DOI:10.12141/j.issn.1000-565X.250369
基于侧信息驱动增强的序列推荐模型
Sequential Recommendation Model Enhanced by Attribute Information-Driven Mechanism
摘要
Abstract
To address the issues that existing sequential recommendation models perform poorly in data-sparse scenarios,and that most current data augmentation methods adopt random perturbation strategies while neglecting item attribute information,thereby causing semantic shift,this paper proposes an attribute-driven sequential recommendation enhancement model named ADAS-Rec.Aiming to enhance the accuracy and robustness of recommendation results,the core idea of the model is to impose explicit constraints on the data augmentation process using multi-dimensional item attribute information.To this end,four collaborative augmentation operators are specifically designed:attribute-similar substitution,attribute-similar insertion,attribute-guided cropping,and attribute-guided masking.Relying on these operators,the model can generate high-quality augmented sequences with diverse content and attribute semantics that align with users'fine-grained preferences,thereby effectively alleviating the data sparsity problem.To further optimize the quality of sequential representations,the model incorporates a frequency-domain filtering module,which removes redundant noise from sequences and highlights key features,providing higher-quality input data for user preference modeling.Finally,the model adopts a dual-attention mechanism that integrates a guided fusion layer and a causal self-attention module,enabling precise mining of users'dynamic preferences and deep temporal dependencies within the augmented and denoised sequences,and completes recommendation predictions based on these preferences and dependencies.Experimental results on three public datasets(Beauty,Sports,and Yelp)demonstrate that the proposed ADAS-Rec model significantly outperforms various state-of-the-art baseline methods across evaluation metrics such as R@10 and NDCG@10.关键词
序列推荐/数据增强/侧信息Key words
sequential recommendation/data augmentation/attribute information分类
信息技术与安全科学引用本文复制引用
马丽,刘文哲..基于侧信息驱动增强的序列推荐模型[J].华南理工大学学报（自然科学版）,2026,54(8):62-73,12.基金项目
河北省教育科学规划一般资助课题(2303121) （2303121）
河北省高等教育教学改革研究项目(2020GJJG227) （2020GJJG227）
河北省高等学校科学技术研究重点项目(ZD2018043) Supported by the General Fund Project of Hebei Provincial Educational Science Planning(2303121),the Research Project on Higher Education Teaching Reform of Hebei Province(2020GJJG227)and the Key Project of Science and Technology Research in Universities of Hebei Province(ZD2018043) （ZD2018043）
