Abstract Evaluation in Information Retrieval (IR) relies on post-hoc empirical procedures,
which are time-consuming and expensive operations. To alleviate this, Query Performance …

In this work, we propose a novel framework to devise features that can be used by Query
Performance Prediction (QPP) models for Neural Information Retrieval (NIR). Using the …

To accelerate software development, much research has been performed to help people
understand and reuse the huge amount of available code resources. Two important tasks …

Web search engines have stored in their logs information about users since they started to
operate. This information often serves many purposes. The primary focus of this survey is on …

This article presents a framework for research and development in user interfaces that
support information seeking. The information-seeking process is described, and each of the …

Query performance prediction aims to estimate the quality of answers that a search system
will return in response to a particular query. In this paper we propose a new family of pre …

Predicting query performance, that is, the effectiveness of a search performed in response to
a query, is a highly important and challenging problem. We present a novel approach to this …

The focus of research on query performance prediction is to predict the effectiveness of a
query given a search system and a collection of documents. If the performance of queries …

The prediction of query performance is an interesting and important issue in Information
Retrieval (IR). Current predictors involve the use of relevance scores, which are time …

Abstract Query Performance Prediction (QPP) has been studied extensively in the IR
community over the last two decades. A by-product of this research is a methodology to …