Query Performance Prediction Using Dimension Importance Estimators

Query Performance Prediction (QPP) tends to fall short when predicting the performance of dense Information Retrieval (IR) systems. Therefore, the research community is investigating QPP approaches designed to synergize with this class of state-of-the-art IR models. At the same time, recent advances concerning dense IR have shown that we can improve the retrieval performance by projecting embeddings in a (query-wise) optimal linear subspace of the dense representation space. The Dimension IMportance Estimation (DIME) framework was proposed to identify such optimal subspaces on a query-by-query basis. In this paper, we illustrate how to design QPP models that rely on measuring the alignment between the query and document representations and the optimal DIME dimensions, based on the hypothesis that good alignment indicates better retrieval performance. We experimentally evaluate the proposed QPPs, showing that our approach outperforms the state-of-the-art when predicting the performance of two commonly used dense encoders, Contriever and TAS-B, on two popular TREC collections, Deep Learning 2019 and 2020.