REGELSON M, FAIN D. Predicting click-through rate using keyword clusters[C]//Proceedings of the Second Workshop on Sponsored Search Auctions, EC 2006. Michigan: ACM, 2006.
[3]
AGARWAL D,BRODER A, CHAKRABARTI D, et al. Estimating rates of rare events at multiple resolutions. Proceedings of the ACM SIGMOD International Conference on Management of Data. Beijing: ACM, 2007: 16-25.
[4]
RICHARDSON M, DOMINIWSKA E, RAGNO R. Predicting Clicks: Estimating the Click-Through Rate for New Ads[C]//Proceedings of the 16th International Conference on World Wide Web, WWW 2007. Banff: ACM, 2007: 521-530.
[5]
CHAKRABARTI D, AGARWAL D, JOSIFOVSKI V. Contextual Advertising by Combining Relevance with Click Feedback[C]//Proceedings of the 17th International Conference on World Wide Web, WWW 2008. Beijing: ACM, 2008: 417-426.
[6]
GOLLAPUDI S, PANIGRAHY R, GOLDSZMIDT M. Inferring Clickthrough Rates on Ads from Click Behavior on Search Results[C]//Proceedings of the Workshop on User Modeling for Web Applications, Fourth International Conference on Web Search and Web Data Mining, WSDM 2011. Hong Kong: ACM,2011.
[7]
YAN J, LIU N, WANG G, et al. How much can Behavioral Targeting Help Online Advertising?[C]//Proceedings of the 18th International Conference on World Wide Web, WWW 2009. Madrid: ACM, 2009: 261-270.
[8]
AHMED A, LOW Y, ALY M, et al. Scalable distributed inference of dynamic user interests for behavioral targeting[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, CA: ACM, 2011: 114-122.
[9]
PEARL J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference [M]. San Mateo, CA: Morgan Kaufmann Publishers, 1988.
[10]
RUSSEL S, NORVIG P. Artificial Intelligence—A Modern Approach [M]. Boston: Pearson Education, Publishing as Prentice-Hall, 2002.
[11]
CHAPELLE O, ZHANG Y. A dynamic Bayesian network click model for web search ranking[C]//Proceedings of the 18th International Conference on World Wide Web, WWW 2009. Madrid: ACM, 2009: 1-10.
