Topic-independent modeling of user knowledge in informational search sessions

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dc.identifier.uri Yu, Ran Tang, Rui Rokicki, Markus Gadiraju, Ujwal Dietze, Stefan 2022-06-16T04:33:24Z 2022-06-16T04:33:24Z 2021
dc.identifier.citation Yu, R.; Tang, R.; Rokicki, M.; Gadiraju, U.; Dietze, S.: Topic-independent modeling of user knowledge in informational search sessions. In: Information Retrieval Journal 24 (2021), Nr. 3, S. 240-268. DOI:
dc.description.abstract Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect to a particular topic or domain. To serve learning needs better, recent research in the field of interactive information retrieval has advocated the importance of moving beyond relevance ranking of search results and considering a user’s knowledge state within learning oriented search sessions. Prior work has investigated the use of supervised models to predict a user’s knowledge gain and knowledge state from user interactions during a search session. However, the characteristics of the resources that a user interacts with have neither been sufficiently explored, nor exploited in this task. In this work, we introduce a novel set of resource-centric features and demonstrate their capacity to significantly improve supervised models for the task of predicting knowledge gain and knowledge state of users in Web search sessions. We make important contributions, given that reliable training data for such tasks is sparse and costly to obtain. We introduce various feature selection strategies geared towards selecting a limited subset of effective and generalizable features. © 2021, The Author(s). eng
dc.language.iso eng
dc.publisher Dordrecht [u.a.] : Springer Science + Business Media B.V.
dc.relation.ispartofseries Information Retrieval Journal 24 (2021), Nr. 3
dc.rights CC BY 4.0 Unported
dc.subject Human–computer interaction eng
dc.subject Knowledge gain eng
dc.subject Online learning eng
dc.subject SAL eng
dc.subject Search as learning eng
dc.subject.ddc 020 | Bibliotheks- und Informationswissenschaft ger
dc.subject.ddc 004 | Informatik ger
dc.title Topic-independent modeling of user knowledge in informational search sessions
dc.type Article
dc.type Text
dc.relation.essn 1573-7659
dc.bibliographicCitation.issue 3
dc.bibliographicCitation.volume 24
dc.bibliographicCitation.firstPage 240
dc.bibliographicCitation.lastPage 268
dc.description.version publishedVersion
tib.accessRights frei zug�nglich

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