A short update from SIGIR09 to announce our recently published work on expert search. This should hopefully be the first of a series of a few posts about SIGIR this year.
In On Perfect Document Rankings for Expert Search (Craig Macdonald & Iadh Ounis), we examine the effect of the document ranking to an expert search engine. Intuitively, improving the topical relevance properties of the document ranking usually leads to an improvement in the performance of the generated ranking of documents. In this poster, we examine the extreme case, by making the document ranking component perfect with respect to topical relevance.
In Usefulness of Click-through data in Expert Search (Craig Macdonald & Ryen White), we examine how user clicks on an intranet search engine can be used as features by an expert search engine. The proposed techniques are based on the voting techniques from the Voting Model, but examine documents clicks instead of weighting model scores. To our knowledge, this is the first work examining how clicks can be integrated into expert search.
Finally, the Voting Model was show-cased in the Expertise Search in Academia using Facets (Duncan McDougall & Craig Macdonald), which demoed AcademTech, a faceted search interface for expert search in academia.
