A personal data store approach for recommender systems: enhancing privacy without sacrificing accuracy

作者：

Highlights：

• Two major limitations of existing recommender systems are privacy and partial view.

• A novel recommender system based on the Open Personal Data Store is proposed.

• Our system enhances privacy significantly without sacrificing accuracy.

摘要

•Two major limitations of existing recommender systems are privacy and partial view.•A novel recommender system based on the Open Personal Data Store is proposed.•Our system enhances privacy significantly without sacrificing accuracy.

论文关键词：Recommender systems,Privacy,Personal data stores

论文评审过程：Received 24 February 2019, Revised 24 June 2019, Accepted 1 August 2019, Available online 8 August 2019, Version of Record 13 August 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112858