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4 Idiots’ Approach for Click-through Rate Prediction 1/15 Team Members 4 Idiots consist of: Name Kaggle ID Affiliation Yu-Chin Juan guestwalk National Taiwan University Wei-Sheng Chin mandora National Taiwan University Yong Zhuang yolicat National Taiwan University Michael Jahrer Michael Jahrer Opera Solutions Our final model is an ensemble of NTU’s model and Michael’s model. Michael’s model is ba
Machine Learning Group at National Taiwan University Contributors Introduction LIBFFM is an open source tool for field-aware factorization machines (FFM). For the formulation of FFM, please see this paper. It has been used to win the top-3 in recent click-through rate prediction competitions (Criteo, Avazu, Outbrain, and RecSys 2015). It supports l2-regularized logistic loss Main features include
3 Idiots’ Approach for Display Advertising Challenge YuChin Juan, Yong Zhuang, and Wei-Sheng Chin NTU CSIE MLGroup 1/1 What This Competition Challenges Us? Predict the click probabilities of impressions. 2/1 Dataset Label I1 I2 · · · I13 C1 C2 · · · C26 1 3 20 · · · 2741 68fd1e64 80e26c9b · · · 4cf72387 0 7 91 · · · 1157 3516f6e6 cfc86806 · · · 796a1a2e 0 12 73 · · · 1844 05db9164 38a947a1 · · · 5
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