A framework for inventor collaboration recommendation system based on network approach
作者：
Highlights：
• Conceived a context-independent Collaboration recommendation system for inventors.
• Existing collaboration recommendation systems-supervised learning based and semantic intensive.
• Minimal Link Semantic (MLS) framework-less semantics, no supervised learning, high usability.
• MLS framework- based on pure network scientometric approach.
• Useful for career enhancement of inventors at all career phases and also for policy makers.
摘要
•Conceived a context-independent Collaboration recommendation system for inventors.•Existing collaboration recommendation systems-supervised learning based and semantic intensive.•Minimal Link Semantic (MLS) framework-less semantics, no supervised learning, high usability.•MLS framework- based on pure network scientometric approach.•Useful for career enhancement of inventors at all career phases and also for policy makers.
论文关键词：Complex networks,Patent analysis,Inventor Collaboration,Co-inventorship,Link prediction,Recommendation system
论文评审过程：Received 22 April 2020, Revised 2 February 2021, Accepted 1 March 2021, Available online 11 March 2021, Version of Record 25 March 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114833
