Print Email Facebook Twitter Topic-independent modeling of user knowledge in informational search sessions Title Topic-independent modeling of user knowledge in informational search sessions Author Yu, Ran (GESIS - Leibniz Institute for the Social Sciences) Tang, Rui (Ping An Technology) Rokicki, Markus (Leibniz University Hannover) Gadiraju, Ujwal (TU Delft Web Information Systems) Dietze, Stefan (GESIS - Leibniz Institute for the Social Sciences; Heinrich Heine University; Leibniz University Hannover) Date 2021 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. Subject Human–computer interactionKnowledge gainOnline learningSALSearch as learning To reference this document use: http://resolver.tudelft.nl/uuid:153fd470-7ef6-4e78-85b3-5fb0fb125977 DOI https://doi.org/10.1007/s10791-021-09391-7 ISSN 1386-4564 Source Information Retrieval, 24 (3), 240-268 Part of collection Institutional Repository Document type journal article Rights © 2021 Ran Yu, Rui Tang, Markus Rokicki, Ujwal Gadiraju, Stefan Dietze Files PDF Yu2021_Article_Topic_inde ... gOfUse.pdf 1.52 MB Close viewer /islandora/object/uuid:153fd470-7ef6-4e78-85b3-5fb0fb125977/datastream/OBJ/view