dc.contributor.author |
Giannopoulos, G |
en |
dc.contributor.author |
Dalamagas, T |
en |
dc.contributor.author |
Eirinaki, M |
en |
dc.contributor.author |
Sellis, T |
en |
dc.date.accessioned |
2014-03-01T02:51:19Z |
|
dc.date.available |
2014-03-01T02:51:19Z |
|
dc.date.issued |
2008 |
en |
dc.identifier.uri |
https://dspace.lib.ntua.gr/xmlui/handle/123456789/35468 |
|
dc.subject |
Clickthrough Data |
en |
dc.subject |
Keyword Search |
en |
dc.subject |
Learning Process |
en |
dc.subject |
Ranking Function |
en |
dc.subject |
Search Engine |
en |
dc.subject |
User Feedback |
en |
dc.title |
Boosting the ranking function learning process using clustering |
en |
heal.type |
conferenceItem |
en |
heal.identifier.primary |
10.1145/1458502.1458523 |
en |
heal.identifier.secondary |
http://dx.doi.org/10.1145/1458502.1458523 |
en |
heal.publicationDate |
2008 |
en |
heal.abstract |
As the Web continuously grows, the results returned by search engines are too many to review. Lately, the prob- lem of personalizing the ranked result list based on user feedback has gained a lot of attention. Such approaches usually require a big amount of user feedback on the results, which is used as training data. In this work, we present |
en |
heal.journalName |
Web Information and Data Management |
en |
dc.identifier.doi |
10.1145/1458502.1458523 |
en |