A context-aware researcher recommendation system for university-industry collaboration on R&D projects
Related Research Unit(s)
|Journal / Publication||Decision Support Systems|
|Online published||5 Sep 2017|
|Publication status||Published - Nov 2017|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85028707628&origin=recordpage|
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.
- University-industry collaboration, Project collaboration, Collaborator identification, Context-aware recommendation
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
Wang, Q, Ma, J, Liao, X & Du, W 2017, 'A context-aware researcher recommendation system for university-industry collaboration on R&D projects', Decision Support Systems, vol. 103, pp. 46-57. https://doi.org/10.1016/j.dss.2017.09.001