| Summary: | Data sparseness and cold start problems caused by unbalanced data distribution restrict the further development of personalized recommendation systems. With the rise of transfer learning technology, cross-domain recommendation based on transfer learning provides possibility to solve such problems. This kind of algorithm can solve the recommendation task in the target domain by transferring appropriate auxiliary domain knowledge which is different but related to the target domain, and improve the performance of target recommendation task in the target domain. The unique advantage of deep learning in non-linear feature learning and representation has greatly improved the performance of deep cross-domain recommendation algorithms. A review of cross-domain recommendation algorithms for knowledge transfer in recent years is carried out. The state-of-the-art algorithms are divided into two categories: cross-domain recommendation and deep cross-domain recommendation. According to different knowledge transfer technologies, they are sorted and summarized separately. And then various algorithms are analyzed and compared in depth from different perspectives such as model interpretability, applicable scenarios, user characteristics, model evaluation, etc. Finally, this paper summarizes the existing problems and dificiencies of existing algorithms, explores passible solutions and forecasts the future development trend.
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