A Bayesian neural model for documents' relevance estimation

We propose QLFusion, an approach based on Quantification Learning (QL) to improve rank fusion performance in Information Retrieval. We first introduce a QL model based on a Bayesian Neural Network to estimate the proportion of relevant documents in a ranked list. The proposed model is trained using a probabilistic loss function formulated specifically for this QL task. Next, we describe a rank fusion algorithm which leverages on this information to merge multiple ranked lists. We compare our approach to various popular rank fusion baselines on multiple collections, showing how the proposed approach outperforms the baselines in several evaluation measures.