The performance of recommender systems in online shopping: A user-centric study

作者：

Highlights：

• We evaluate four approaches to online product search in an extensive user study with real data.

• The approaches that we study exploit novel preference relaxation methods to recommend products.

• We compare the results of a user experiment with simulations, using two large sets of products.

• We show that a particular preference relaxation method improves the decisions of online shoppers.

摘要

•We evaluate four approaches to online product search in an extensive user study with real data.•The approaches that we study exploit novel preference relaxation methods to recommend products.•We compare the results of a user experiment with simulations, using two large sets of products.•We show that a particular preference relaxation method improves the decisions of online shoppers.

论文关键词：NR,No Relaxation,SR,Standard Relaxation,SBR,Soft-Boundary Preference Relaxation,SBRADD,Soft-Boundary Preference Relaxation with Addition,SBRREP,Soft-Boundary Preference Relaxation with Replacement,Recommender systems,Decision theory,Preference relaxation,E-commerce

论文评审过程：Available online 27 April 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.04.022