A contextual-bandit approach to personalized news article recommendation

@article{Li2010ACA,
  title={A contextual-bandit approach to personalized news article recommendation},
  author={Lihong Li and Wei Chu and John Langford and Robert E. Schapire},
  journal={ArXiv},
  year={2010},
  volume={abs/1003.0146}
}
Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. [] Key Method Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to…

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