计算机工程与应用2026，Vol.62Issue(12)：196-205,10.DOI:10.3778/j.issn.1002-8331.2504-0097
融合对抗网络和对比学习的三重桥梁跨域推荐
Triple Bridge Cross Domain Recommendation Integrating Adversarial Networks and Contras-tive Learning
摘要
Abstract
A triple bridge cross domain recommendation model(ACTBCDR)that integrates adversarial networks and con-trastive learning is proposed to address the problems of negative preference modeling deficiency and single user prefer-ence transfer strategy in existing meta learning cross domain recommendation methods.Positive and negative feature encoders are designed to obtain users'positive and negative preferences,in order to capture more comprehensive user preferences.To solve the problem of a single user preference migration strategy,a triple bridge of personalized preferences,source domain common preferences,and target domain common preferences is constructed,and a dynamic gating mecha-nism is adopted to achieve multi-granularity feature fusion.A domain adversarial training module is designed that achieves cross domain user representation alignment through gradient reversal layer(GRL),and the common preference bridge is mapped between the source and target domains to the same space by comparing loss constraints,reducing domain differences.Experiments on the benchmark datasets of Book,Music,and Movie in Amazon have shown that ACTBCDR performs well in MAE,RMSE,AUC,and NDCG@10.The indicators exceed the baseline method by 7.28%,8.29%,1.68%,and 6.91%respectively,verifying the effectiveness of the model.关键词
跨域推荐/生成性对抗网络/元学习/冷启动问题Key words
cross domain recommendation/generative adversarial networks/meta learning/cold-start分类
信息技术与安全科学引用本文复制引用
杨海燕,梅红岩,胡思雨..融合对抗网络和对比学习的三重桥梁跨域推荐[J].计算机工程与应用,2026,62(12):196-205,10.基金项目
国家自然科学基金(12371363) （12371363）
辽宁省教育厅科研项目(JYTMS20230869) （JYTMS20230869）
辽宁省科技计划联合计划(重点研发计划项目)(2025JH2/101800245). （重点研发计划项目）
