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Integrating neurophysiologic relevance feedback in intent modeling for information retrieval
Author(s) -
Jacucci Giulio,
Barral Oswald,
Daee Pedram,
Wenzel Markus,
Serim Baris,
Ruotsalo Tuukka,
Pluchino Patrik,
Freeman Jonathan,
Gamberini Luciano,
Kaski Samuel,
Blankertz Benjamin
Publication year - 2019
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24161
Subject(s) - relevance (law) , computer science , relevance feedback , task (project management) , neurophysiology , electroencephalography , artificial intelligence , information retrieval , machine learning , human–computer interaction , psychology , neuroscience , image retrieval , management , political science , law , economics , image (mathematics)
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).