expertise drift and query expansion in expert search
Pseudo-relevance feedback, or query expansion, has been shown to improve retrieval performance in the adhoc retrieval task. In such a scenario, a few top-ranked documents are assumed to be relevant, and these are then used to expand and refine the initial user query, such that it retrieves a higher quality ranking of documents. However, there has been little work in applying query expansion in the expert search task. In this setting, query expansion is applied by assuming a few top-ranked candidates have relevant expertise, and using these to expand the query. Nevertheless, retrieval is not improved as expected using such an approach. We show that the success of the application of query expansion is hindered by the presence of topic drift within the profiles of experts that the system considers. In this work, we demonstrate how topic drift occurs in the expert profiles, and moreover, we propose three measures to predict the amount of drift occurring in an expert's profile. Finally, we suggest and evaluate ways of enhancing query expansion in expert search using our new insights. Our results show that, once topic drift has been anticipated, query expansion can be successfully applied in a general manner in the expert search task.
query expansion topic drift expert search information retrieval expertise modelling expert finding