A generalized stereotype learning approach and its instantiation in trust modeling

作者：

Highlights：

• Generalized fuzzy semantic framework is designed for users to model unknown entities.

• Fuzzy process generalizes over numeric attributes while semantic process over nominal attributes.

• The framework is demonstrated on decision tree (FSDT), which is instantiated in trust modeling.

• Experiments on real data verify the effectiveness of our approach.

摘要

•Generalized fuzzy semantic framework is designed for users to model unknown entities.•Fuzzy process generalizes over numeric attributes while semantic process over nominal attributes.•The framework is demonstrated on decision tree (FSDT), which is instantiated in trust modeling.•Experiments on real data verify the effectiveness of our approach.

论文关键词：User modeling,Stereotype trust model,Fuzzy semantic framework,E-commerce

论文评审过程：Received 31 October 2017, Revised 30 April 2018, Accepted 25 June 2018, Available online 28 June 2018, Version of Record 3 July 2018.

论文官网地址：https://doi.org/10.1016/j.elerap.2018.06.004