Addressing delayed feedback for continuous training with neural networks in CTR prediction

@article{Ktena2019AddressingDF,
  title={Addressing delayed feedback for continuous training with neural networks in CTR prediction},
  author={S. Ktena and Alykhan Tejani and Lucas Theis and Pranay K. Myana and D. Dilipkumar and Ferenc Husz{\'a}r and Steven Yoo and Wenzhe Shi},
  journal={Proceedings of the 13th ACM Conference on Recommender Systems},
  year={2019}
}
One of the challenges in display advertising is that the distribution of features and click through rate (CTR) can exhibit large shifts over time due to seasonality, changes to ad campaigns and other factors. [...] Key Method We also discuss the engineering cost associated with implementing each loss function in a production environment. Finally, we carried out online experiments with the top performing methods, in order to validate their performance in a continuous training scheme. While training on 668…Expand
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
  • Haoming Li, Feiyang Pan, +6 authors Qing He
  • Computer Science
  • SIGIR
  • 2021
Dual Learning Algorithm for Delayed Feedback in Display Advertising
A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback
Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback
RLNF: Reinforcement Learning based Noise Filtering for Click-Through Rate Prediction
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
Extended Factorization Machines for Sequential Recommendation
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Modeling delayed feedback in display advertising
Practical Lessons from Predicting Clicks on Ads at Facebook