ABSTRACT
Traditionally click models predict click-through rate (CTR) of an advertisement (ad) independent of other ads. Recent researches however indicate that the CTR of an ad is dependent on the quality of the ad itself but also of the neighboring ads. Using historical click-through data of a commercially available ad server, we identify two types (competing and collaborating) of influences among sponsored ads and further propose a novel click-model, Full Relation Model (FRM), which explicitly models dependencies between ads. On a test data, FRM shows significant improvement in CTR prediction as compared to earlier click models.
- O. Chapelle and Y. Zhang. A dynamic bayesian network click model for web search ranking. In Proceedings of the 18th international conference on World wide web, WWW '09, pages 1--10, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey. An experimental comparison of click position-bias models. In Proceedings of the international conference on Web search and web data mining, WSDM '08, pages 87--94, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- R. Cristi. Hegel on Freedom and Authority. 2005.Google Scholar
- G. E. Dupret and B. Piwowarski. A user browsing model to predict search engine click data from past observations. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pages 331--338, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- F. Guo, C. Liu, A. Kannan, T. Minka, M. Taylor, Y.-M. Wang, and C. Faloutsos. Click chain model in web search. In Proceedings of the 18th international conference on World wide web, WWW '09, pages 11--20, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- F. Guo, C. Liu, and Y. M. Wang. Efficient multiple-click models in web search. In Proceedings of the second international conference on Web search and data mining, WSDM '09, pages 124--131, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133--142. ACM, 2002. Google ScholarDigital Library
- C. Liu, F. Guo, and C. Faloutsos. Bbm: bayesian browsing model from petabyte-scale data. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pages 537--546, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- R. Srikant, S. Basu, N. Wang, and D. Pregibon. User browsing models: relevance versus examination. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pages 223--232, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- C. Xiong, T. Wang, W. Ding, Y. Shen, and T. Liu. Relational click prediction for sponsored search. In Proceedings of the fifth ACM international conference on Web search and data mining, pages 493--502. ACM, 2012. Google ScholarDigital Library
- W. Xu, E. Manavoglu, and E. Cantu-Paz. Temporal click model for sponsored search. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '10, pages 106--113, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- F. Zhong, D. Wang, G. Wang, W. Chen, Y. Zhang, Z. Chen, and H. Wang. Incorporating post-click behaviors into a click model. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '10, pages 355--362, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- Z. A. Zhu, W. Chen, T. Minka, C. Zhu, and Z. Chen. A novel click model and its applications to online advertising. In Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pages 321--330, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
Index Terms
- Do ads compete or collaborate?: designing click models with full relationship incorporated
Recommendations
Predicting CTR of new ads via click prediction
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementPredicting CTR of ads on the search result page is an urgent topic. The reason for this is that choosing the right advertisement greatly affects revenue of the search engine and advertisers and user's satisfaction. For ads with the large click history ...
Learning the click-through rate for rare/new ads from similar ads
SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrievalAds on the search engine (SE) are generally ranked based on their Click-through rates (CTR). Hence, accurately predicting the CTR of an ad is of paramount importance for maximizing the SE's revenue. We present a model that inherits the click information ...
The Impact of Competing Ads on Click Performance in Sponsored Search
Our research examines the impact of competing ads on click performance of an ad in sponsored search. We use a unique data set of 1,267 advertiser keyword pairs with differing ad quality related to 360 keywords from a search engine to evaluate the click ...
Comments