Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/www2008/paper/645> ?p ?o. }
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- 645 creator massimiliano-ciaramita.
- 645 creator vanessa-murdock.
- 645 creator vassilis-plachouras.
- 645 type InProceedings.
- 645 label "Online Learning from Click Data for Sponsored Search".
- 645 sameAs 645.
- 645 abstract "Sponsored search is one of the enabling technologies for today's Web search engines. It corresponds to matching and showing ads related to the user query on the search engine results page. Users are likely to click on topically related ads and the advertisers pay only when a user clicks on their ad. Hence, it is important to be able to predict if an ad is likely to be clicked, and maximize the number of clicks. We investigate the sponsored search problem from a machine learning perspective with respect to three main sub-problems: how to use click data for training and evaluation, which online learning framework is more suitable for the task, and which features are useful for existing models. We perform a large scale evaluation based on data from a commercial Web search engine. Our results show that it is possible to learn and evaluate directly and exclusively on click data. Even with a small number of features, we can improve the ranking of ads over an information retrieval baseline. To the best of our knowledge this is the first study in sponsored search using learning and evaluation from click-data.".
- 645 hasAuthorList authorList.
- 645 hasTopic World_Wide_Web.
- 645 isPartOf proceedings.
- 645 keyword "Click data".
- 645 keyword "Machine learning".
- 645 keyword "Ranking".
- 645 keyword "Semantics".
- 645 keyword "Sponsored search".
- 645 title "Online Learning from Click Data for Sponsored Search".