A dimension importance estimation-based framework for query performance prediction

Recent developments in the dense Information Retrieval (IR) domain have shown the ties between the links between the latent dimensions and the retrieval effectiveness. In detail, Dimension IMportance Estimators (DIMEs) have been proposed to identify a subspace of the original dense representation space where the retrieval is more effective. On a different research line, Query Performance Prediction (QPP) techniques focus on determining the performance of an IR system in the absence of human-made relevance judgements. In this extended abstract, we illustrate the effectiveness of QPP models that exploit the DIME mechanisms to formulate the predictions. In particular, the QPPs illustrated here rely on measuring how much the retrieval insists on dimensions considered relevant by a DIME model to establish how likely the retrieval was effective. To evaluate the effectiveness of the proposed approach, we consider two well-known IR collections, TREC Deep Learning'19 and'20, and dense IR approaches, TAS-B and Contriever, and show that the DIME-based QPPs achieve state-of-the-art results when predicting the performance of both IR systems on both collections.