TY  - CONF
AU  - Navarro Bullock, Beate
AU  - Jäschke, Robert
AU  - Hotho, Andreas
A2  - 
T1  - Tagging data as implicit feedback for learning-to-rank
T2  - Proceedings of the ACM WebSci Conference
PB  - 
CY  - New York, NY, USA
PY  - 2011/06
M2  - 
VL  - 
IS  - 
SP  - 1
EP  - 4
UR  - http://journal.webscience.org/463/
M3  - 
KW  - 2011
KW  - bookmarking
KW  - folksonomy
KW  - letor
KW  - myown
KW  - ranking
KW  - social
KW  - tagging
L1  - 
SN  - 
N1  - 
N1  - 
AB  - Learning-to-rank methods automatically generate ranking functions which can be used for ordering unknown resources according to their relevance for a specific search query. The training data to construct such a model consists of features describing a document-query-pair as well as relevance scores indicating how important the document is for the query. In general, these relevance scores are derived by asking experts to manually assess search results or by exploiting user search behaviour such as click data. The human evaluation of ranking results gives explicit relevance scores, but it is expensive to obtain. Clickdata can be logged from the user interaction with a search engine, but the feedback is noisy. In this paper, we want to explore a novel source of implicit feedback for web search: tagging data. Creating relevance feedback from tagging data leads to a further source of implicit relevance feedback which helps improve the reliability of automatically generated relevance scores and therefore the quality of learning-to-rank models.
ER  -