Efficient optimization of multiple recommendation quality factors according to individual user tendencies
作者：
Highlights：
• An efficient post-processing scheme for recommendation lists is proposed.
• It can adjust quality factors, like diversity, of a list to match user tendencies.
• Compromises on accuracy are kept low.
• The method is compared with other post-processing algorithms from the literature.
• It can be used to build novel fine-grained personalization approaches.
摘要
•An efficient post-processing scheme for recommendation lists is proposed.•It can adjust quality factors, like diversity, of a list to match user tendencies.•Compromises on accuracy are kept low.•The method is compared with other post-processing algorithms from the literature.•It can be used to build novel fine-grained personalization approaches.
论文关键词：Recommender systems,Quality factors,User-specific optimization,Trade-offs
论文评审过程：Received 11 July 2016, Revised 23 March 2017, Accepted 24 March 2017, Available online 29 March 2017, Version of Record 5 April 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.03.055
