Integrating neurophysiologic relevance feedback in intent modeling for information retrieval

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Jacucci , G , Barral , O , Daee , P , Wenzel , M , Serim , B , Ruotsalo , T , Pluchino , P , Freeman , J , Gamberini , L , Kaski , S & Blankertz , B 2019 , ' Integrating neurophysiologic relevance feedback in intent modeling for information retrieval ' , Journal of the Association for Information Science and Technology , vol. 70 , no. 9 , pp. 917-930 . https://doi.org/10.1002/asi.24161

Title: Integrating neurophysiologic relevance feedback in intent modeling for information retrieval
Author: Jacucci, Giulio; Barral, Oswald; Daee, Pedram; Wenzel, Markus; Serim, Baris; Ruotsalo, Tuukka; Pluchino, Patrik; Freeman, Jonathan; Gamberini, Luciano; Kaski, Samuel; Blankertz, Benjamin
Contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Aalto University
Date: 2019-09
Language: eng
Number of pages: 14
Belongs to series: Journal of the Association for Information Science and Technology
ISSN: 2330-1635
URI: http://hdl.handle.net/10138/304867
Abstract: The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).
Subject: 113 Computer and information sciences
3112 Neurosciences
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