Improving Pseudo Relevance Feedback in the Divergence from Randomness Model

Abstract

In an earlier analysis of Pseudo Relevance Feedback (PRF) models by Clinchant and Gaussier (2013), five desirable properties that PRF models should satisfy were formalised. Also, modifications to two PRF models were proposed in order to improve compliance with the desirable properties. These resulted in improved retrieval effectiveness. In this study, we introduce a sixth property that we believe PRF models should satisfy. We also extend the earlier exercise to Bo1, a standard PRF model. Experimental results on the robust, wt10g and gov2 datasets show that the proposed modifications yield improvements in effectiveness.