Mapping preferences into Euclidean space

作者：

Highlights：

• New approach for a real world problem: preference learning via matrix factorization.

• Comparison between factorization and SVM tensorial approaches.

• Visual representation of the solution in an Euclidean space.

摘要

•New approach for a real world problem: preference learning via matrix factorization.•Comparison between factorization and SVM tensorial approaches.•Visual representation of the solution in an Euclidean space.

论文关键词：Preference learning,Matrix factorization,Graphical representations,Learning to order,Visualization

论文评审过程：Received 8 November 2014, Revised 1 July 2015, Accepted 3 July 2015, Available online 19 July 2015, Version of Record 29 August 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.07.013