A cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains

作者：

Highlights：

• The proposed model can effectively transfer knowledge from auxiliary domains, and evaluate the importance of auxiliary domains.

• The proposed model can well alleviate the sparsity problem.

• The proposed model can well solve the cold-start problem.

• The proposed model can address the scenario of multiple auxiliary domains easily.

摘要

•The proposed model can effectively transfer knowledge from auxiliary domains, and evaluate the importance of auxiliary domains.•The proposed model can well alleviate the sparsity problem.•The proposed model can well solve the cold-start problem.•The proposed model can address the scenario of multiple auxiliary domains easily.

论文关键词：Cross-domain collaborative filtering,Feature expansion,Funk-SVD decomposition,Classification,Latent factor space

论文评审过程：Received 6 October 2018, Revised 25 January 2019, Accepted 18 May 2019, Available online 18 May 2019, Version of Record 24 May 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.05.030