Ranking experts using author-document-topic graphs

Expert search or recommendation involves the retrieval of people (experts) in response to a query and on occasion, a given set of constraints. In this paper, we address expert recommendation in academic domains that are different from web and intranet environments studied in TREC. We propose and study graph-based models for expertise retrieval with the objective of enabling search using either a topic (e.g. "Information Extraction") or a name (e.g. "Bruce Croft"). We show that graph-based ranking schemes despite being "generic" perform on par with expert ranking models specific to topic-based and name-based querying.

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