A Comparative Study of Pseudo Relevance Feedback for Ad-hoc Retrieval

This paper presents an initial investigation in the relative effectiveness of different popular pseudo relevance feedback (PRF) methods. The retrieval performance of relevance model, and two KL-divergence-based divergence from randomness (DFR) feedback methods generalized from Rocchio's algorithm, are compared by extensive experiments on standard TREC test collections. Results show that a KL-divergence based DFR method (denoted as KL1), combined with the classical Rocchio's algorithm, has the best retrieval effectiveness out of the three methods studied in this paper.