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

摘要： 双目标跨域推荐作为跨域推荐技术的关键分支，凭借双向协同优化机制同步提升源域与目标域推荐效能，在电子商务、视频分发、新闻资讯等领域应用广泛。本文首先介绍了跨域推荐中的领域层次结构与重叠场景特征，并从知识嵌入方式方法角度，详细阐述协同过滤嵌入、图嵌入和自监督学习嵌入的核心原理，对比分析了其技术特性与适用场景；从领域对齐技术角度，着重对比了基于特征映射、解耦表示学习、元学习及联邦学习的四类主流领域对齐方案，总结了其技术差异与实践价值。其次，系统梳理了双目标跨域推荐中的主流数据集与评估指标，结合不同跨域场景特性，明确各数据集与指标的适配准则。最后，基于当前研究现状与技术挑战，对双目标跨域推荐的未来发展方向进行前瞻性展望。

关键词: 双目标跨域推荐, 嵌入方法, 领域对齐