A context-aware researcher recommendation system for university-industry collaboration on R&D projects
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › Not applicable › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 46-57 |
Journal / Publication | Decision Support Systems |
Volume | 103 |
Online published | 5 Sep 2017 |
Publication status | Published - Nov 2017 |
Link(s)
Abstract
University-industry collaboration plays an important role in the success of R&D projects. One of the main challengesof university-industry collaboration is the identification of suitable partners. Due to the information asymmetry problem, it is difficult for companies to identify researchers from universities for collaboration on their R&D projects. Various expert recommendation systems (e.g., question responder recommenders and co-author recommenders) have been proposed, but they fail to characterize companies' needs in identifying suitable researchers. This paper proposes a context-aware researcher recommendation system to encourage university-industry collaboration on industrial R&D projects. The system has two modules: an offline preparation module and an online recommendation module. In the offline preparation module, candidate researchers are identified in advance to improve the efficiency of the context-aware recommendation. In the online recommendation module, contextual information (i.e., R&D projects) is captured from a social network platform, and then, candidate researchers are recommended based on a contextual trust analysis model, which combines the expertise relevance, quality, and trust relations of researchers to profile and evaluate candidate researchers for the R&D project collaboration. An offline experiment and a user study are conducted to evaluate the effectiveness of the proposedrecommendation system. The results show that the proposed method achieves better performance than the baseline methods.
Research Area(s)
- University-industry collaboration, Project collaboration, Collaborator identification, Context-aware recommendation
Citation Format(s)
A context-aware researcher recommendation system for university-industry collaboration on R&D projects. / Wang, Qi; Ma, Jian; Liao, Xiuwu ; Du, Wei.
In: Decision Support Systems, Vol. 103, 11.2017, p. 46-57.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › Not applicable › peer-review