Expert Recommendation Based on Collaborative Filtering in Subject Research
| Title | Expert Recommendation Based on Collaborative Filtering in Subject Research |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Li, Gaochao, Jin, Xin, Wang, Zhonghua, Chen, Xunxun, Wu, Xiao |
| Conference Name | Proceedings of the 2018 International Conference on Information Science and System |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6421-8 |
| Keywords | expert recommendation, human factors, pubcrawl, recommender systems, resilience, Resiliency, Scalability, Topology of subject |
| Abstract | This article implements a method for expert recommendation based on collaborative filtering. The recommendation model extracts potential evaluation experts from historical data, figures out the relevance between past subjects and current subjects, obtains the evaluation experience index and personal ability index of experts, calculates the relevance of research direction between experts and subjects and finally recommends the most proper experts. |
| URL | https://dl.acm.org/citation.cfm?doid=3209914.3209939 |
| DOI | 10.1145/3209914.3209939 |
| Citation Key | li_expert_2018 |