ITM Web Conf.
Volume 7, 20163rd Annual International Conference on Information Technology and Applications (ITA 2016)
|Number of page(s)||6|
|Section||Session 4: Information System and its Applications|
|Published online||21 November 2016|
- Iresearch. Analysis of the overall development of China’s online advertising market[EB/OL], http://www.iyunying.org/seo/sjfx/12636.html [Google Scholar]
- Zhou A Y, Zhou M Q, Gong X Q. Computational advertising: A data-centric comprehensive web application[J]. Jisuanji Xuebao(Chinese Journal of Computers), 2011, 34(10):pp.1805–1819. [Google Scholar]
- Joachims T. Optimizing search engines using click through data[C] // Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002: pp.133–142. [CrossRef] [Google Scholar]
- Graepel T, Candela J Q, Borchert T, et al. Web-scale bayesian click-through rate estimation for sponsored search advertising in microsoft’s bing search engine[C] // Proceedings of the 27th International Conference on Machine Learning (ICML-10). 2010: pp.13–20. [Google Scholar]
- 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. ACM, 2009: pp.1–10. [CrossRef] [Google Scholar]
- Dave K, Varma V. Predicting the Click-Through Rate for Rare/New Ads[R]. Center for Search and Information Extraction Lab International Institute of Information Technology Hyderabad, INDIA, April 2010. [Google Scholar]
- Richardson M, Dominowska E, Ragno R. Predicting clicks: estimation the click-through rate for new ads[C] // Proceedings of the 16th international conference on World Wide Web. ACM, 2007: pp.521–530. [CrossRef] [Google Scholar]
- Agarwal D, Broder A Z, Chakrabarti D, et al. estimation rates of rare events at multiple resolutions[C] // Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2007: pp.16–25. [CrossRef] [Google Scholar]
- Agarwal D, Agrawal R, Khanna R, et al. estimation rates of rare events with multiple hierarchies through scalable log-linear models[C]//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010:pp. 213–222. [CrossRef] [Google Scholar]
- Zhang W V, Jones R. Comparing click logs and editorial labels for training query rewriting[C]// WWW 2007 Workshop on Query Log Analysis: Social And Technological Challenges. 2007. [Google Scholar]
- Cheng H, Cantu-Paz E. Personalized click estimation in sponsored search.[C] // Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010:pp.351–360. [CrossRef] [Google Scholar]
- Zhang Y, Dai H, Xu C, et al. Sequential Click estimation for Sponsored Search with Recurrent Neural Networks[C]. AAAI 2014: pp.1369–1375. [Google Scholar]
- LogarithmicLoss[EB/OL]. https://www.kaggle.com/wiki/LogarithmicLoss. [Google Scholar]
- Zeiler M D. ADADELTA: An adaptive learning rate method[J]. arXiv preprint arXiv:1212.5701, 2012. [Google Scholar]
- Wager S, Wang S, Dropout Liang P. Training as Adaptive Regularization[J]. Advances in Neural Information Processing Systems, 2013: pp.351–359. [Google Scholar]
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