Query Performance Prediction Through Retrieval Coherency

Post-retrieval Query Performance Prediction (QPP) methods benefit from the characteristics of the retrieved set of documents to determine query difficulty. While existing works have investigated the relation between query and retrieved document spaces, as well as retrieved document scores, the association between the retrieved documents themselves, referred to as coherency, has not been extensively investigated for QPP. We propose that the coherence of the retrieved documents can be formalized as a function of the characteristics of a network that represents the associations between these documents. Based on experiments on three corpora, namely Robust04, Gov2 and ClueWeb09 and their TREC topics, we show that our coherence measures outperform existing metrics in the literature and are able to significantly improve the performance of state of the art QPP methods.