The Relevance Feedback (RF) process relies on accurate and real-time relevance
estimation of feedback documents to improve retrieval performance. Since collecting explicit …

This article presents a comprehensive meta-review of the intersection between Brain-
Computer Interface (BCI) technologies and the Metaverse, emphasizing the enhancement of …

Relevance assessment, a crucial Human-computer Information Retrieval (HCIR) aspect,
denotes how well retrieved information meets the user's information need (IN). Recently …

We propose an image-classification method to predict the perceived-relevance of text
documents from eye-movements. An eye-tracking study was conducted where participants …

Relevance is an essential concept in Information Retrieval (IR). Recent studies using brain
imaging have significantly contributed towards the understanding of this concept, but only as …

Despite advances in the past few decades in studying what kind of queries users input to
search engines and how to suggest queries for the users, the fundamental question of what …

Generally, medical content-based image retrieval (CBIR) systems select low-level visual
features as image descriptors. However, these descriptors fail to provide clues for …

The use of implicit relevance feedback from neurophysiology could deliver effortless
information retrieval. However, both computing neurophysiologic responses and retrieving …

Objective. The use of an electroencephalogram (EEG) anticipation-related component, the
expectancy wave (E-wave), in brain–machine interaction was proposed more than 50 years …

Browsing the web for finding answers to questions has become pervasive in our everyday
lives. When users search the web to satisfy their information-needs, their on-screen eye …