关键词: 双目标跨域推荐, 嵌入方法, 领域对齐

Abstract: 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 the core principles of collaborative 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. Secondly, the main data sets and evaluation indicators in the dual objective cross-domain recommendation are systemically sorted out, and the adaptation criteria of each data set and indicator are defined in combination with the characteristics of different cross-domain scenarios. Finally, based on the current research status and technical challenges, the future development direction of dual objective cross-domain recommendation is prospectively prospected.

Key words: dual target cross-domain recommendation, embedding method, domain alignment