Corpus ID: 13240553

Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction

@inproceedings{Tscher2012EnsembleOC,
  title={Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction},
  author={Andreas T{\"o}scher and M. Jahrer and Jeong-Yoon Lee},
  year={2012}
}
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… Expand
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  • Computer Science
  • 2020 5th International Conference on Computer Science and Engineering (UBMK)
  • 2020
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