计算机工程与应用2025，Vol.61Issue(6)：244-253,10.DOI:10.3778/j.issn.1002-8331.2406-0357
跨视图的用户多行为对比推荐模型
Cross-View Contrastive Model for User Multi-Behavior Recommendation
摘要
Abstract
Traditional recommendation models often struggle to fully exploit and leverage the diversity and correlations within multi-type behavioral data,resulting in suboptimal recommendation performance.Currently,there are two main challenges in multi-behavior recommendation:(1)how to decouple behaviors at the item level to better separate consis-tency and difference signals among behaviors;(2)how to better enhance behavioral differences and consistent interests.This paper proposes a cross-view contrastive model for user multi-behavior recommendation(CVCM),which decomposes user interests across multi-behaviors and leverages contrastive learning between different behavioral interest views of users and specific behavioral interests across users.Specifically,an interest decomposer is firstly employed to disentangle behavior-specific interests and behavior-independent interests from multi-behavior interaction data.Subsequently,a cross-view contrastive learning module is designed to enhance behavioral diversity by contrasting a user's original view with its weighted transformed view.Finally,a multi-user contrastive learning module is utilized to extract consistent features across different behaviors.Evaluation results on three real datasets,namely Rec-Tmall,Taobao,and Beibei,show that,compared to the best baseline,the improvements in NDCG@10 for the three datasets are 13.99%,4.98%,and 17.23%,respectively.关键词
多行为推荐/多行为兴趣/对比学习Key words
multi-behavior recommendation/multi-behavior interest/contrastive learning分类
信息技术与安全科学引用本文复制引用
吴瑕,王绍卿,张尧..跨视图的用户多行为对比推荐模型[J].计算机工程与应用,2025,61(6):244-253,10.基金项目
山东省自然科学基金(ZR2020MF147,ZR2021MF017). （ZR2020MF147,ZR2021MF017）
