Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction
نویسندگان
چکیده
The challenge for Track 2 of the KDD Cup 2012 competition was to predict the click-through rate (CTR) of web advertisements given information about the ad, the query and the user. Our solution comprised an ensemble of models, combined using an artificial neural network. We built collaborative filters, probability models, and feature engineered models to predict CTRs. In addition, we developed a few models which directly optimized AUC, including the collaborative filters and ANN models. These models were then blended using AUC optimized ANN such that the final output of the system had significantly improved performance over the constituent models on test data. We achieved an AUC score of 0.80824 on the private leaderboard and finished second in the competition.
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