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- DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks. Shuangfei Zhai, Keng-hao Chang, Ruofei Zhang, Zhongfei(Mark) Zhang. SIGKDD, 2016.
- Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising. Weinan ZhangM Tianxiong Zhou, Jun Wang, Jian Xu. SIGKDD, 2016.
- Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction. Weinan Zhang, Tianming Du, Jun Wang.
- Fast Computation of Posterior Mode in Multi-Level Hierarchical Models. Liang Zhang, D. Agarwal. Neural Information Processing Systems Foundation, 2008.
- Estimating Rates of Rare Events with Multiple Hierarchies through Scalable Log-linear Models. D. Agarwal, R. Agrawal, R. Khanna, N, Kota. SIGKDD, 2010.
- Estimating Rates of Rare Events at Multiple Resolutions. D. Agarwal, A. Broder, D. Chakrabarti, D. Diklic, V. Josifovski, M. Sayyadian. SIGKDD, 2007
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- Post-Click Conversion Modeling and Analysis for Non-Guaranteed Delivery Display Advertising. R. Rosales, Haibin Cheng, E. Manavoglu. WSDM, 2012.
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- Promoting Positive Post-Click Experience for in-stream Yahoo Gemini Users. M. Lalmas, J. Lehmann, G. Shaked, F. Silvestri and G. Tolomei. SIGKDD, 2015.
- Improving Post-Click User Engagement on Native Ads via Survival Analysis.. N. Barbieri, F. Silvestri, M. Lalmas. WWW, 2016.
- Prediting Pre-Click Quality for Native Advertisements. Ke Zhou, M. Redi, Andy Haines, Mounia Lalmas. WWW, 2016.
- Data-driven Multi-touch Attribution Models. Xuhui Shao, Lexin Li. SIGKDD, 2011.
- Causally Motivated Attribution for Online Advertising. B. Dalessandro, C. Perlish, O. Stitelman, F. Provost. Workshop paper, 2012.
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