双目标跨域推荐中嵌入方法与领域对齐技术研究综述OA

As a key branch of cross-domain recommendation technology,dual-target cross-domain recommendation improves the efficiency of source domain and target domain recommendation synchronously by virtue of a two-way collaborative optimization mechanism,and is widely used in e-commerce,video distribution,news and information and other fields.This paper first introduces the domain hierarchy and overlapping scene characteristics in the cross-domain recommendation,and from the perspective of knowledge embedding methods,elaborates on the core principles of collab-orative filtering embedding,graph embedding,and self-monitoring learning embedding,and compares and analyzes their technical characteristics and applicable scenarios.From the perspective of domain alignment technology,four mainstream domain alignment schemes based on feature mapping,decoupled representation learning,meta-learning and federated learning are emphatically compared,and their technical differences and practical values are summarized.Then,the main datasets and evaluation indicators in the dual-target cross-domain recommendation are systemically sorted out,and the adaptation criteria of each dataset and indicator are defined in combination with the characteristics of different cross-domain scenarios.Finally,based on the current research status and technical challenges,future development direction of dual-target cross-domain recommendation is prospected.