Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 240-268 |
| Seitenumfang | 29 |
| Fachzeitschrift | Information retrieval journal |
| Jahrgang | 24 |
| Ausgabenummer | 3 |
| Frühes Online-Datum | 16 März 2021 |
| Publikationsstatus | Veröffentlicht - Juni 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Sozialwissenschaften (insg.)
- Bibliotheks- und Informationswissenschaften
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in: Information retrieval journal, Jahrgang 24, Nr. 3, 06.2021, S. 240-268.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Topic-independent modeling of user knowledge in informational search sessions
AU - Yu, Ran
AU - Tang, Rui
AU - Rokicki, Markus
AU - Gadiraju, Ujwal
AU - Dietze, Stefan
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Human–computer interaction
KW - Knowledge gain
KW - Online learning
KW - SAL
KW - Search as learning
UR - http://www.scopus.com/inward/record.url?scp=85102949366&partnerID=8YFLogxK
U2 - 10.1007/s10791-021-09391-7
DO - 10.1007/s10791-021-09391-7
M3 - Article
AN - SCOPUS:85102949366
VL - 24
SP - 240
EP - 268
JO - Information retrieval journal
JF - Information retrieval journal
SN - 1386-4564
IS - 3
ER -