Interactive Intent Modeling for Exploratory Search

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http://hdl.handle.net/10138/308883

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Ruotsalo , T , Peltonen , J , Eugster , M J A , Glowacka , D , Floréen , P , Myllymäki , P , Jacucci , G & Kaski , S 2018 , ' Interactive Intent Modeling for Exploratory Search ' , ACM Transactions on Information Systems , vol. 36 , no. 4 , pp. 44:1-46 . https://doi.org/10.1145/3231593

Title: Interactive Intent Modeling for Exploratory Search
Author: Ruotsalo, Tuukka; Peltonen, Jaakko; Eugster, Manuel J.A.; Glowacka, Dorota; Floréen, Patrik; Myllymäki, Petri; Jacucci, Giulio; Kaski, Samuel
Contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Intelligent Interactive Information Access research group / Patrik Floréen
University of Helsinki, Department of Computer Science
University of Helsinki, Ubiquitous Interaction research group / Giulio Jacucci
University of Helsinki, Aalto University
Date: 2018-10
Number of pages: 46
Belongs to series: ACM Transactions on Information Systems
ISSN: 1046-8188
URI: http://hdl.handle.net/10138/308883
Abstract: Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.
Subject: 113 Computer and information sciences
proactive search
user intent modeling
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