Expertise Recommender System for Scientific Community

Finding experts in academics as well as in enterprises is an important practical problem. Both manual and automated approaches are employed and have their own pros and cons. On one hand, the manual approaches need extensive human efforts but the quality of data is good, on the other hand, the automated approaches normally do not need human efforts but the quality of service is not as good as in the manual approaches. Furthermore, the automated approaches normally use only one metric to measure the expertise of an individual. For example, for finding experts in academia, the number of publications of an individual is used to discover and rank experts. This paper illustrates both manual and automated approaches for finding experts and subsequently proposes and implements an automated approach for measuring expertise profile in academia. The proposed approach incorporates multiple metrics for measuring an overall expertise level. To visualize a rank list of experts, an extended hyperbolic visualization technique is proposed and implemented. Furthermore, the discovered experts are pushed to users based on their local context. The research has been implemented for Journal of Universal Computer Science (J. UCS) and is available online for the users of J. UCS.