Integrating neurophysiologic relevance feedback in intent modeling for information retrieval

dc.contributor.authorJacucci, Giulio
dc.contributor.authorBarral, Oswald
dc.contributor.authorDaee, Pedram
dc.contributor.authorWenzel, Markus A.
dc.contributor.authorSerim, Baris
dc.contributor.authorRuotsalo, Tuukka
dc.contributor.authorPluchino, Patrik
dc.contributor.authorFreeman, Jonathan
dc.contributor.authorGamberini, Luciano
dc.contributor.authorKaski, Samuel
dc.contributor.authorBlankertz, Benjamin
dc.date.accessioned2019-12-11T11:57:55Z
dc.date.available2019-12-11T11:57:55Z
dc.date.issued2019-03-12
dc.description.abstractThe 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).en
dc.description.sponsorshipEC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeen
dc.identifier.eissn2330-1643
dc.identifier.issn2330-1635
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10479
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9431
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.otherneurophysiologyen
dc.subject.otherneuroadaptive IRen
dc.subject.otherEEGen
dc.titleIntegrating neurophysiologic relevance feedback in intent modeling for information retrievalen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1002/asi.24161en
dcterms.bibliographicCitation.issue9en
dcterms.bibliographicCitation.journaltitleJournal of the Association for Information Science and Technologyen
dcterms.bibliographicCitation.originalpublishernameWileyen
dcterms.bibliographicCitation.originalpublisherplaceHobokenen
dcterms.bibliographicCitation.pageend930en
dcterms.bibliographicCitation.pagestart917en
dcterms.bibliographicCitation.volume70en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Neurotechnologiede
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Neurotechnologiede
tub.affiliation.instituteInst. Softwaretechnik und Theoretische Informatikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

Files

Original bundle
Now showing 1 - 1 of 1
Loading…
Thumbnail Image
Name:
Jacucci_et_al_2019.pdf
Size:
1.23 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.9 KB
Format:
Item-specific license agreed upon to submission
Description:

Collections