Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | CIKM 2021 Workshops |
| Untertitel | Proceedings of the CIKM 2021 Workshops co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) |
| Publikationsstatus | Veröffentlicht - 2021 |
| Veranstaltung | 2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021 - Gold Coast, Australien Dauer: 1 Nov. 2021 → 5 Nov. 2021 |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 3052 |
| ISSN (Print) | 1613-0073 |
Abstract
Users frequently search on the Web to fulfill information needs with learning intent. In this context, usefulness of the search results depends strongly on the knowledge state of the user. In order to satisfy learning needs effectively, it is necessary to take users' knowledge gain and knowledge state within learning-oriented Web search sessions into account. Previous works studied the use of supervised models to predict a user's knowledge gain and knowledge state. However, the impact of knowledge domains of the search topics on a user's learning process have not been adequately explored. In this paper, we suggest domain detection techniques for search sessions and build domain-specific knowledge prediction models accordingly. Experimental evaluation results demonstrate that our approach outperforms the state-of-the-art baseline.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
CIKM 2021 Workshops: Proceedings of the CIKM 2021 Workshops co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021). 2021. (CEUR Workshop Proceedings; Band 3052).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Domain-Specific Modeling of User Knowledge in Informational Search Sessions
AU - Tang, Rui
AU - Yu, Ran
AU - Rokicki, Markus
AU - Ewerth, Ralph
AU - Dietze, Stefan
N1 - Funding Information: Part of this work is supported by the Leibniz Association, Germany (Leibniz Competition 2018, funding line "Collaborative Excellence", project SALIENT [K68/2017]).
PY - 2021
Y1 - 2021
N2 - Users frequently search on the Web to fulfill information needs with learning intent. In this context, usefulness of the search results depends strongly on the knowledge state of the user. In order to satisfy learning needs effectively, it is necessary to take users' knowledge gain and knowledge state within learning-oriented Web search sessions into account. Previous works studied the use of supervised models to predict a user's knowledge gain and knowledge state. However, the impact of knowledge domains of the search topics on a user's learning process have not been adequately explored. In this paper, we suggest domain detection techniques for search sessions and build domain-specific knowledge prediction models accordingly. Experimental evaluation results demonstrate that our approach outperforms the state-of-the-art baseline.
AB - Users frequently search on the Web to fulfill information needs with learning intent. In this context, usefulness of the search results depends strongly on the knowledge state of the user. In order to satisfy learning needs effectively, it is necessary to take users' knowledge gain and knowledge state within learning-oriented Web search sessions into account. Previous works studied the use of supervised models to predict a user's knowledge gain and knowledge state. However, the impact of knowledge domains of the search topics on a user's learning process have not been adequately explored. In this paper, we suggest domain detection techniques for search sessions and build domain-specific knowledge prediction models accordingly. Experimental evaluation results demonstrate that our approach outperforms the state-of-the-art baseline.
KW - Informational search
KW - Knowledge gain
KW - Search as learning
UR - http://www.scopus.com/inward/record.url?scp=85122885935&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85122885935
T3 - CEUR Workshop Proceedings
BT - CIKM 2021 Workshops
T2 - 2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021
Y2 - 1 November 2021 through 5 November 2021
ER -