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Filters
A collaborative filtering approach to ad recommendation using the query-ad click graph
2009
Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09
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Search engine logs contain a large amount of click-through data that can be leveraged as soft indicators of relevance. In this paper we address the sponsored search retrieval problem which is to find and rank relevant ads to a search query. We propose a new technique to determine the relevance of an ad document for a search query using click-through data. The method builds on a collaborative filtering approach to discover new ads related to a query using a click graph. It is implemented on a
doi:10.1145/1645953.1646267
dblp:conf/cikm/AnastasakosHKR09
fatcat:3dmfwdft6jhjrgmgkiwc7lob24
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... ph with several million edges and scales to larger sizes easily. The proposed method is compared to three different baselines that are state-of-the-art for a commercial search engine. Evaluations on editorial data indicate that the model discovers many new ads not retrieved by the baseline methods. The ads from the new approach are on average of better quality than the baselines.
Music Information Retrieval: Recent Developments and Applications
2014
Foundations and Trends in Information Retrieval
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Tasos Anastasakos, Dustin Hillard, Sanjay Kshetramade, and Hema Raghavan. A collaborative filtering approach to ad recommendation using the query-ad click graph. ...
doi:10.1561/1500000045
fatcat:zlqzjh2h5vgpdcj6f5yze64fk4
A large-scale active learning system for topical categorization on the web
2010
Proceedings of the 19th international conference on World wide web - WWW '10
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ACKNOWLEDGMENTS The authors would like to thank Dun Liu, Wanlin Pang and Sanjay Kshetramade for their invaluable help in deploying the prototype. ...
doi:10.1145/1772690.1772771
dblp:conf/www/RajanYGR10
fatcat:d7nuzyl4lfb6rczio3as64ppi4