Journal of Frontiers of Computer Science and Technology ›› 2020, Vol. 14 ›› Issue (11): 1813-1827.DOI: 10.3778/j.issn.1673-9418.2006053

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Advances and Perspectives on Knowledge Transfer Based Cross-Domain Recom-mendation

REN Hao, LIU Baisong, SUN Jinyang   

  1. Faculty of Information Science and Engineering, Ningbo University, Ningbo, Zhejiang 315211, China
  • Online:2020-11-01 Published:2020-11-09



  1. 宁波大学 信息科学与工程学院,浙江 宁波 315211


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.

Key words: data sparsity, transfer learning, knowledge transfer, cross-domain recommendation



关键词: 数据稀疏, 迁移学习, 知识迁移, 跨领域推荐