Retrieval models in information retrieval are used to rank documents for typically under-
specified queries. Today machine learning is used to learn retrieval models from click logs …

Information retrieval (IR) models often employ complex variations in term weights to compute
an aggregated similarity score of a query-document pair. Treating IR models as black-boxes …

It has been shown that the interpretability of search results is enhanced when query aspects
covered by documents are explicitly provided. However, existing work on aspect-oriented …

Deep neural networks have recently shown promise in the ad-hoc retrieval task. However,
such models have often been based on one field of the document, for example considering …

A key problem in information retrieval is understanding the latent intention of a user's under-
specified query. Retrieval models that are able to correctly uncover the query intent often …

This paper proposes a novel approach towards better interpretability of a trained text-based
ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text …

The query-performance prediction task has been described as estimating retrieval
effectiveness in the absence of relevance judgments. The expectations throughout the years …

This tutorial presents explainable information retrieval (ExIR), an emerging area focused on
fostering responsible and trustworthy deployment of machine learning systems in the context …

We propose a model that leverages the millions of clicks received by web search engines, to
predict document relevance. This allows the comparison of ranking functions when clicks …

Dealing with unjudged documents (" holes") in relevance assessments is a perennial
problem when evaluating search systems with offline experiments. Holes can reduce the …