Item recommendation by predicting bipartite network embedding of user preference

作者：

Highlights：

• Recommendation method resistant to the data sparsity problem is proposed.

• Using a Kalman filter to predict the change in user preference.

• Evaluated on e-commerce dataset from a real world online shopping website.

摘要

•Recommendation method resistant to the data sparsity problem is proposed.•Using a Kalman filter to predict the change in user preference.•Evaluated on e-commerce dataset from a real world online shopping website.

论文关键词：Bipartite network embedding,Item recommendation,Kalman filtering,Time-aware Recommendation Method,User Preference

论文评审过程：Received 14 August 2019, Revised 8 February 2020, Accepted 25 February 2020, Available online 29 February 2020, Version of Record 8 March 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113339