计算机与现代化Issue(1)：37-43,7.DOI:10.3969/j.issn.1006-2475.2025.01.007
基于属性异质图的多目标对抗跨领域推荐
Multi-Target Adversarial Cross-domain Recommendation Based on Attributed Heterogeneous Graph
摘要
Abstract
Cross-domain recommendation technique plays a crucial role in addressing the challenges of cold start and data spar-sity in recommendation systems.However,existing methods often make the simplifying assumption that users'preferences re-main consistent across different domains.This assumption overlooks the inherent heterogeneity of user preferences,resulting in suboptimal recommendation performance.Furthermore,most approaches focus solely on recommendation tasks between do-mains,lacking a natural extension to multi-domain scenarios.In this spaper,we propose a novel approach called MAAH(Multi-Target Adversarial Cross-domain Recommendation based on Attributed Heterogeneous Graph).The method leverages the struc-tural information encoded in an attributed heterogeneous graph to represent users and items.By capturing both the homogeneity and heterogeneity of user behavior across domains,MAAH provides a more comprehensive understanding of user preferences.Importantly,we introduce adversarial learning to further integrate and discriminate user preferences,thereby enhancing recom-mendation effectiveness in each domain.Notably,MAAH achieves multi-objective cross-domain recommendation,addressing the limitations of existing methods.Experimental results on publicly available datasets demonstrate that MAAH effectively miti-gates data sparsity and offers a promising solution to the cold start problem in cross-domain recommendation scenarios.关键词
推荐系统/跨领域/属性异质图/图嵌入/对抗学习Key words
recommendation system/cross-domain/attribute heterogeneity graph/graph embedding/adversarial learning分类
信息技术与安全科学引用本文复制引用
袁杰,朱焱..基于属性异质图的多目标对抗跨领域推荐[J].计算机与现代化,2025,(1):37-43,7.基金项目
四川省科技计划项目(2019YFSY0032) （2019YFSY0032）
