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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 adoi:10.1145/1645953.1646267 dblp:conf/cikm/AnastasakosHKR09 fatcat:3dmfwdft6jhjrgmgkiwc7lob24
more »... 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.
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
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