On Information-Theoretic Document-Person Associations for Expert Search in Academia

State-of-the-art expert search approaches rely on document-person associations to infer the expertise of a candidate person for a given query. Such associations have traditionally been modeled as boolean variables, indicating whether or not a candidate authored a document, and further normalized to penalize prolific authorships. In this paper, we address expert search in academia, where the authorship of a document can be determined with reasonable certainty. In contrast to traditional approaches, we propose to model associations as non-boolean variables, reflecting the probability that a document is informative of the expertise of a candidate. Moreover, we introduce an alternative normalization scheme that measures how discriminative a particular document-person association is in light of all associations involving either the document or the person. Through a large-scale user study with academic experts from several areas of knowledge, we demonstrate the suitability of the proposed association and normalization schemes to improve the effectiveness of a state-of-the-art expert search approach.