Click-Through Prediction for Sponsored Search Advertising with Hybrid Models
Xingxing Wang, Shijie Lin, Dongying Kong, Liheng Xu, Qiang Yan, Siwei Lai, Liang Wu, Alvin Chin, Guibo Zhu, Heng Gao, Yang Wu, Danny Bickson, Yuanfeng Du, Neng Gong, Chengchun Shu, Shuang Wang, Liu Kang, Shuren Li, Jun Zhao, Fei Tan · 2012
In this paper, we report our approach of KDD Cup 2012 track 2 to predicting the click-through rate (CTR) of advertisements. To accurately predict the CTR of an ad is important for commercial search engine companies for deciding the click prices and the order of impressions. We first implemented three existing methods including Online Bayesian Probit Regression (BPR), Support Vector Machine (SVM) and Latent Factor Model (LFM). In order to fully exploit the training set, several Maximum Likelihood Estimation(MLE)based methods are then proposed to model the instances which appear frequently in the training set. Each of the individual models is optimized by selecting the most descriptive features. We propose a rank-based ensemble method which greatly improves the results of our model and our final submission is based on BPR, SVM and MLE.