Co-Occurrence-Based Diffusion for Expert Search on the Web
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ABSTRACT
Expert search has been studied in different contexts, e.g., enterprises, academic communities. We examine a general expert search problem: searching experts on the web, where millions of webpages and thousands of names are considered. It has mainly two challenging issues: 1) webpages could be of varying quality and full of noises; 2) The expertise evidences scattered in webpages are usually vague and ambiguous. We propose to leverage the large amount of co-occurrence information to assess relevance and reputation of a person name for a query topic. The co-occurrence structure is modeled using a hypergraph, on which a heat diffusion based ranking algorithm is proposed. Query keywords are regarded as heat sources, and a person name which has strong connection with the query (i.e., frequently co-occur with query keywords and co-occur with other names related to query keywords) will receive most of the heat, thus being ranked high.
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