Investigating the suboptimality and instability of pseudo-relevance feedback

Abstract

Although Pseudo-Relevance Feedback (PRF) techniques improve average retrieval performance at the price of high variance, not much is known about their optimality and the reasons for their instability. In this work, we study more than 800 topics from several test collections including the TREC Robust Track and show that PRF techniques are highly suboptimal, i.e. they do not make the fullest utilization of pseudo-relevant documents and under-perform. A careful selection of expansion terms from the pseudo-relevant document with the help of an oracle can actually improve retrieval performance dramatically (by > 60%). Further, we show that instability in PRF techniques is mainly due to wrong selection of expansion terms from the pseudo-relevant documents. Our findings emphasize the need to revisit the problem of term selection to make a break through in PRF.