A Unified Approach for Learning Expertise and Authority in Digital Libraries

Managing individual expertise is a major concern within any industrial-wide organization. If previous works have extensively studied the related expertise and authority profiling issues, they assume a semantic independence of these two key concepts. In digital libraries, state-of-the-art models generally summarize the researchers’ profile by using solely textual information. Consequently, authors with a large amount of publications are mechanically fostered to the detriment of less prolific ones with probably higher expertise. To overcome this drawback we propose to merge the two representations of expertise and authority and balance the results by capturing a mutual reinforcement principle between these two notions. Based on a graph representation of the library, the expert profiling task is formulated as an optimization problem where latent expertise and authority representations are learned simultaneously, unbiasing the expertise scores of individuals with a large amount of publications. The proposal is instanciated on a public scientific bibliographic dataset where researchers’ publications are considered as a source of evidence of individuals’ expertise and citation relations as a source of authoritative signals. Results from our experiments conducted over the Microsoft Academic Search database demonstrate significant efficiency improvement in comparison with state-of-the-art models for the expert retrieval task.