Collaborative filtering over evolution provenance data for interactive visual data exploration
作者：
Highlights：
• Proposal of a visual data exploration system with content and collaborative recommendations.
• Proposal of several merge techniques to aggregate users exploration sessions in a multi-user graph.
• Proposal of several optimizations to improve collaborative-filtering recommendation computation.
• Quantitative evaluation of collaborative-filtering recommendations techniques.
• Qualitative evaluation of users experiences when visually exploring data using our system.
摘要
•Proposal of a visual data exploration system with content and collaborative recommendations.•Proposal of several merge techniques to aggregate users exploration sessions in a multi-user graph.•Proposal of several optimizations to improve collaborative-filtering recommendation computation.•Quantitative evaluation of collaborative-filtering recommendations techniques.•Qualitative evaluation of users experiences when visually exploring data using our system.
论文关键词：Visual data exploration,Provenance,Recommendations
论文评审过程：Received 4 August 2019, Revised 3 June 2020, Accepted 7 August 2020, Available online 18 August 2020, Version of Record 26 August 2020.
论文官网地址：https://doi.org/10.1016/j.is.2020.101620
