Robust query-specific pseudo feedback document selection for query expansion

In document retrieval using pseudo relevance feedback, after initial ranking, a fixed number of top-ranked documents are selected as feedback to build a new expansion query model. However, very little attention has been paid to an intuitive but critical fact that the retrieval performance for different queries is sensitive to the selection of different numbers of feedback documents. In this paper, we explore two approaches to incorporate the factor of query-specific feedback document selection in an automatic way. The first is to determine the "optimal" number of feedback documents with respect to a query by adopting the clarity score and cumulative gain. The other approach is that, instead of capturing the optimal number, we hope to weaken the effect of the numbers of feedback document, i.e., to improve the robustness of the pseudo relevance feedback process, by a mixture model. Our experimental results show that both approaches improve the overall retrieval performance.