Automatic preference learning on numeric and multi-valued categorical attributes

作者：

Highlights：

• Learning preferences implicitly is a challenging task in the design of recommenders.

• Our approach infers preferences by analyzing choices without any explicit feedback.

• Choices considered are defined by numerical and multi-valued categorical criteria.

摘要

•Learning preferences implicitly is a challenging task in the design of recommenders.•Our approach infers preferences by analyzing choices without any explicit feedback.•Choices considered are defined by numerical and multi-valued categorical criteria.

论文关键词：Recommender systems,Preference learning,Aggregation operators,Fuzzy sets,Ranking

论文评审过程：Received 11 December 2012, Revised 11 November 2013, Accepted 13 November 2013, Available online 22 November 2013.

论文官网地址：https://doi.org/10.1016/j.knosys.2013.11.012