Negative relevance feedback for exploratory search with visual interactive intent modeling

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorPeltonen, Jaakkoen_US
dc.contributor.authorStrahl, Jonathanen_US
dc.contributor.authorFloréen, Patriken_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorCentre of Excellence in Computational Inference, COINen
dc.contributor.groupauthorProfessorship Kaski Samuelen
dc.contributor.groupauthorHelsinki Institute for Information Technology (HIIT)en
dc.contributor.groupauthorProbabilistic Machine Learningen
dc.contributor.groupauthorMyllymäki Petri group (HIIT)en
dc.date.accessioned2017-06-20T11:27:30Z
dc.date.available2017-06-20T11:27:30Z
dc.date.issued2017-03-07en_US
dc.description.abstractIn difficult information seeking tasks, the majority of topranked documents for an initial query may be non-relevant, and negative relevance feedback may then help find relevant documents. Traditional negative relevance feedback has been studied on document results; we introduce a system and interface for negative feedback in a novel exploratory search setting, where continuous-valued feedback is directly given to keyword features of an inferred probabilistic user intent model. The introduced system allows both positive and negative feedback directly on an interactive visual interface, by letting the user manipulate keywords on an optimized visualization of modeled user intent. Feedback on the interactive intent model lets the user direct the search: Relevance of keywords is estimated from feedback by Bayesian inference, influence of feedback is increased by a novel propagation step, documents are retrieved by likelihoods of relevant versus non-relevant intents, and the most relevant keywords (having the highest upper confidence bounds of relevance) and the most non-relevant ones (having the smallest lower confidence bounds of relevance) are shown as options for further feedback. We carry out task-based information seeking experiments with real users on difficult real tasks; we compare the system to the nearest state of the art baseline allowing positive feedback only, and show negative feedback significantly improves the quality of retrieved information and user satisfaction for difficult tasks.en
dc.description.versionPeer revieweden
dc.format.extent11
dc.format.extent149-159
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationPeltonen, J, Strahl, J & Floréen, P 2017, Negative relevance feedback for exploratory search with visual interactive intent modeling . in IUI 2017 - Proceedings of the 22nd International Conference on Intelligent User Interfaces . ACM, pp. 149-159, International Conference on Intelligent User Interfaces, Limassol, Cyprus, 13/03/2017 . https://doi.org/10.1145/3025171.3025222en
dc.identifier.doi10.1145/3025171.3025222en_US
dc.identifier.isbn9781450343480
dc.identifier.otherPURE UUID: c612b4da-b621-4d21-9643-ab3f6c490814en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/c612b4da-b621-4d21-9643-ab3f6c490814en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85016503337&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/13633016/p149_peltonen.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/26988
dc.identifier.urnURN:NBN:fi:aalto-201706205712
dc.language.isoenen
dc.relation.ispartofInternational Conference on Intelligent User Interfacesen
dc.relation.ispartofseriesIUI 2017 - Proceedings of the 22nd International Conference on Intelligent User Interfacesen
dc.rightsopenAccessen
dc.subject.keywordDifficult queriesen_US
dc.subject.keywordInteractive exploratory searchen_US
dc.subject.keywordLanguage modelen_US
dc.subject.keywordNegative relevance feedbacken_US
dc.subject.keywordNovelty in information retrievalen_US
dc.subject.keywordPresentation of retrieval resultsen_US
dc.subject.keywordQuery intenten_US
dc.subject.keywordQuery reformulationen_US
dc.subject.keywordSearch interfacesen_US
dc.subject.keywordUser intent modelen_US
dc.titleNegative relevance feedback for exploratory search with visual interactive intent modelingen
dc.typeConference article in proceedingsfi
dc.type.versionpublishedVersion
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