计算机工程2026，Vol.52Issue(6)：132-140,9.DOI:10.19678/j.issn.1000-3428.0070193
基于随机自注意力和动量对比学习的自监督序列推荐方法
Self-Supervised Sequence Recommendation Method Based on Random Self-Attention and Momentum Contrastive Learning
摘要
Abstract
Sequence recommendation utilizes user historical sequence behavior to model user interests and provide content recommendations,and is commonly employed in sectors such as news,advertising,and e-commerce.Self-supervised sequence recommendation based on contrastive learning is a current research hotspot.However,real sequence data are dynamically uncertain,and sampling biases exist in contrastive learning,which limit the performance of recommendations.To mitigate these issues,this paper proposes a self-supervised sequence recommendation method based on stochastic self-attention and momentum contrastive learning.Stochastic self-attention is used to alleviate the uncertainty of sequence dynamics,and momentum contrastive learning is used to mitigate the sampling bias problem in contrastive learning.To validate the performance of the model,experiments are conducted on three datasets:Beauty,Office,Yelp,and Toys.The results demonstrate that the proposed method outperforms other baseline models across several metrics,including HR@K and NDCG@K,indicating significant improvements in both accuracy and robustness.关键词
序列推荐/动量对比学习/Wasserstein距离/自监督学习/自注意力Key words
sequence recommendation/momentum contrastive learning/Wasserstein distance/self-supervised learning/self-attention分类
信息技术与安全科学引用本文复制引用
余正涛,孙资钦,张勇丙,高盛祥,黄于欣,谭凯文..基于随机自注意力和动量对比学习的自监督序列推荐方法[J].计算机工程,2026,52(6):132-140,9.基金项目
国家自然科学基金联合基金重点项目(U23A20388) （U23A20388）
国家自然科学基金(U21B2027,62376111,62266028,62266027) （U21B2027,62376111,62266028,62266027）
云南省重点研发计划(202303AP140008,202401BC070021,202103AA080015) （202303AP140008,202401BC070021,202103AA080015）
云南省科技人才与平台计划(202105AC160018) （202105AC160018）
云南省基础研究项目(202301AT070393). （202301AT070393）
