论文信息 - Research on Advertising Click-Through Rate Prediction Model Based on Ensemble Learning - 字舞流文

Research on Advertising Click-Through Rate Prediction Model Based on Ensemble Learning

Hong Cheng | Wenjie Pan | Xiaojuan He

[1] Chih-Jen Lin,et al. Field-aware Factorization Machines for CTR Prediction , 2016, RecSys.

[2] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .

[3] Aoying Zhou,et al. Computational Advertising: A Data-Centric Comprehensive Web Application: Computational Advertising: A Data-Centric Comprehensive Web Application , 2011 .

[4] Matthew Richardson,et al. Predicting clicks: estimating the click-through rate for new ads , 2007, WWW '07.

[5] Tie-Yan Liu,et al. Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks , 2014, AAAI.

[6] Zhang Hua,et al. CTR prediction for online advertising based on a features conjunction model , 2018 .

[7] Liu Wei-yi,et al. Click-through rate prediction of online advertisements based on probabilistic graphical model , 2013 .

[8] Joaquin Quiñonero Candela,et al. Practical Lessons from Predicting Clicks on Ads at Facebook , 2014, ADKDD'14.

[9] Steffen Rendle,et al. Factorization Machines with libFM , 2012, TIST.

[10] Vasudeva Varma,et al. Learning the click-through rate for rare/new ads from similar ads , 2010, SIGIR.