Using community preference for overcoming sparsity and cold-start problems in collaborative filtering system offering soft ratings

作者：

Highlights：

• This paper proposes a new collaborative filtering recommender system offering soft ratings.

• In the system, community preferences are used for overcoming sparsity and cold-start problems.

• The new system is experimentally tested on Flixster data set.

摘要

•This paper proposes a new collaborative filtering recommender system offering soft ratings.•In the system, community preferences are used for overcoming sparsity and cold-start problems.•The new system is experimentally tested on Flixster data set.

论文关键词：Recommender systems,E-commerce,Soft ratings,Community preferences,Dempster-Shafer theory

论文评审过程：Received 23 January 2017, Revised 1 October 2017, Accepted 6 October 2017, Available online 7 October 2017, Version of Record 24 October 2017.

论文官网地址：https://doi.org/10.1016/j.elerap.2017.10.002