Details
| Original language | English |
|---|---|
| Title of host publication | Joint Proceedings of the 10th International Workshop on News Recommendation and Analytics (INRA’22) and the Third International Workshop on Investigating Learning During Web Search (IWILDS‘22) |
| Subtitle of host publication | co-located with the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’22) |
| Pages | 60-68 |
| Number of pages | 9 |
| Publication status | Published - 2022 |
| Event | INRA 2022 : 10th International Workshop on News Recommendation and Analytics - Madrid, Spain Duration: 11 Jul 2022 → 15 Jul 2022 |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | CEUR Workshop Proceedings |
| Volume | 3411 |
| ISSN (Print) | 1613-0073 |
Abstract
Web search has often been used as a starting point to learn. Search as Learning (SAL) research aims at supporting learning activities through techniques such as user interface optimization, retrieval, and ranking. In this work, we investigate the possibility of re-ranking search engine results towards learning to improve the overall knowledge gain of the learner. We make two contributions: (1) proposing a framework for re-ranking search results by attributing the overall knowledge gain to viewed documents in the session. (2) Applying this framework to a SAL evaluation dataset. We show that the ranking can be significantly improved with respect to knowledge gain by using ranking and content features.
ASJC Scopus subject areas
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Joint Proceedings of the 10th International Workshop on News Recommendation and Analytics (INRA’22) and the Third International Workshop on Investigating Learning During Web Search (IWILDS‘22) : co-located with the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’22). 2022. p. 60-68 (CEUR Workshop Proceedings; Vol. 3411).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Learning to Rank for Knowledge Gain
AU - Rokicki, Markus
AU - Yu, Ran
AU - Hienert, Daniel
N1 - Funding Information: This work is partially funded by the Leibniz Association, Germany (Leibniz Competition 2018, funding line "Collaborative Excellence", project SALIENT [K68/2017]).
PY - 2022
Y1 - 2022
N2 - Web search has often been used as a starting point to learn. Search as Learning (SAL) research aims at supporting learning activities through techniques such as user interface optimization, retrieval, and ranking. In this work, we investigate the possibility of re-ranking search engine results towards learning to improve the overall knowledge gain of the learner. We make two contributions: (1) proposing a framework for re-ranking search results by attributing the overall knowledge gain to viewed documents in the session. (2) Applying this framework to a SAL evaluation dataset. We show that the ranking can be significantly improved with respect to knowledge gain by using ranking and content features.
AB - Web search has often been used as a starting point to learn. Search as Learning (SAL) research aims at supporting learning activities through techniques such as user interface optimization, retrieval, and ranking. In this work, we investigate the possibility of re-ranking search engine results towards learning to improve the overall knowledge gain of the learner. We make two contributions: (1) proposing a framework for re-ranking search results by attributing the overall knowledge gain to viewed documents in the session. (2) Applying this framework to a SAL evaluation dataset. We show that the ranking can be significantly improved with respect to knowledge gain by using ranking and content features.
UR - http://www.scopus.com/inward/record.url?scp=85162897859&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85162897859
T3 - CEUR Workshop Proceedings
SP - 60
EP - 68
BT - Joint Proceedings of the 10th International Workshop on News Recommendation and Analytics (INRA’22) and the Third International Workshop on Investigating Learning During Web Search (IWILDS‘22)
T2 - INRA 2022 : 10th International Workshop on News Recommendation and Analytics
Y2 - 11 July 2022 through 15 July 2022
ER -