Directing and Combining Multiple Queries for Exploratory Search by Visual Interactive Intent Modeling

dc.contributorAalto Universityen
dc.contributor.authorStrahl, Jonathanen_US
dc.contributor.authorPeltonen, Jaakkoen_US
dc.contributor.authorFloreen, Patriken_US
dc.contributor.departmentProbabilistic Machine Learningen_US
dc.contributor.departmentTampere University of Technologyen_US
dc.contributor.departmentHelsinki Institute for Information Technology (HIIT)en_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.editorArdito, Carmeloen_US
dc.contributor.editorLanzilotti, Rosaen_US
dc.contributor.editorMalizia, Alessioen_US
dc.contributor.editorPetrie, Helenen_US
dc.contributor.editorPiccinno, Antonioen_US
dc.contributor.editorDesolda, Giuseppeen_US
dc.contributor.editorInkpen, Korien_US
dc.description.abstractIn interactive information-seeking, a user often performs many interrelated queries and interactions covering multiple aspects of a broad topic of interest. Especially in difficult information-seeking tasks the user may need to find what is in common among such multiple aspects. Therefore, the user may need to compare and combine results across queries. While methods to combine queries or rankings have been proposed, little attention has been paid to interactive support for combining multiple queries in exploratory search. We introduce an interactive information retrieval system for exploratory search with multiple simultaneous search queries that can be combined. The user is able to direct search in the multiple queries, and combine queries by two operations: intersection and difference, which reveal what is relevant to the user intent of two queries, and what is relevant to one but not the other. Search is directed by relevance feedback on visualized user intent models of each query. Operations on queries act directly on the intent models inferring a combined user intent model. Each combination yields a new result (ranking) and acts as a new search that can be interactively directed and further combined. User experiments on difficult information-seeking tasks show that our novel system with query operations yields more relevant top-ranked documents in a shorter time than a baseline multiple-query system.en
dc.description.versionPeer revieweden
dc.identifier.citationStrahl , J , Peltonen , J & Floreen , P 2021 , Directing and Combining Multiple Queries for Exploratory Search by Visual Interactive Intent Modeling . in C Ardito , R Lanzilotti , A Malizia , A Malizia , H Petrie , A Piccinno , G Desolda & K Inkpen (eds) , Human-Computer Interaction – INTERACT 2021 - 18th IFIP TC 13 International Conference, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 12934 LNCS , Springer , pp. 514-535 , International Conference on Human-Computer Interaction , Bari , Italy , 30/08/2021 .
dc.identifier.otherPURE UUID: 5e822601-da9d-47df-bc2d-5ad206acc7e8en_US
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dc.relation.ispartofIFIP TC.13 International Conference on Human-Computer Interactionen
dc.relation.ispartofseriesIFIP Conference on Human-Computer Interaction INTERACT 2021en
dc.relation.ispartofseriesLecture Notes in Computer Scienceen
dc.relation.ispartofseriesVolume 12934en
dc.subject.keywordinteractive information retrievalen_US
dc.subject.keywordinformation visualizationen_US
dc.subject.keywordexploratory searchen_US
dc.subject.keywordintent modelingen_US
dc.subject.keywordquery combinationen_US
dc.titleDirecting and Combining Multiple Queries for Exploratory Search by Visual Interactive Intent Modelingen
dc.typeConference article in proceedingsfi