arXiv 28 Sep 2020 · Machine Learning · publishedProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022) · 5 citations (OpenAlex)
arXiv:2009.13092 · PDF · DOI · OpenAlex · Extracted main text
We consider training a binary classifier under delayed feedback (DF learning). For example, in the conversion prediction in online ads, we initially receive negative samples that clicked the ads but did not buy an item; subsequently, some samples among them buy an item then change to positive. In the setting of DF learning, we observe samples over time, then learn a classifier at some point. We initially receive negative samples; subsequently, some samples among them change to positive. This problem is conceivable in various real-world applications such as online advertisements, where the user action takes place long after the first click. Owing to the delayed feedback, naive classification of the positive and negative samples returns a biased classifier. One solution is to use samples that have been observed for more than a certain time window assuming these samples are correctly labeled. However, existing studies reported that simply using a subset of all samples based on the time window assumption does not perform well, and that using all samples along with the time window assumption improves empirical performance. We extend these existing studies and propose a method with the unbiased and convex empirical risk that is constructed from all samples under the time window assumption. To demonstrate the soundness of the proposed method, we provide experimental results on a synthetic and open dataset that is the real traffic log datasets in online advertising.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Olivier Chapelle (2014) Modeling Delayed Feedback in Display Advertising. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Di… | 1.000 | 11 | 4 | 100% |
| 2 | Shota Yasui, Gota Morishita, Fujita Komei, and Masashi Shibata (2020) A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback. In Proceedings of The Web Conference 2020. As… self | 1.000 | 6 | 4 | 100% |
| 3 | Ryuichi Kiryo, Gang Niu, Marthinus C du Plessis, and Masashi Sugiyama (2017) Positive-Unlabeled Learning with Non-Negative Risk Estimator. In Advances in Neural Information Processing Systems, Vol. 30. Cur… | 0.855 | 8 | 5 | 62% |
| 4 | Masahiro Kato and Takeshi Teshima (2021) Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation. In Proceedings of the 38th International… self | 0.737 | 5 | 3 | 40% |
| 5 | Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, De… (2019) Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR Prediction. In Proceedings of the 13th ACM Confe… | 0.737 | 3 | 3 | 67% |
| 6 | Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin… (2014) Practical Lessons from Predicting Clicks on Ads at Facebook. In Proceedings of the Eighth International Workshop on Data Mining… | 0.737 | 3 | 2 | 100% |
| 7 | Marthinus Christoffel du Plessis, Gang Niu, and Masashi Sugiyama (2015) Convex Formulation for Learning from Positive and Unlabeled Data. In Proceedings of the 32nd International Conference on Machine… | 0.644 | 4 | 2 | 50% |
| 8 | Yuya Yoshikawa and Yusaku Imai (2018) A Nonparametric Delayed Feedback Model for Conversion Rate Prediction | 0.511 | 2 | 1 | 100% |
| 9 | Alekh Agarwal and John C Duchi (2011) Distributed Delayed Stochastic Optimization. In Advances in Neural Information Processing Systems, Vol. 24. Curran Associates, Inc | 0.405 | 1 | 1 | 100% |
| 10 | Nicolò Cesa-Bianchi, Claudio Gentile, and Yishay Mansour (2019) Delay and Cooperation in Nonstochastic Bandits | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 42 scored citations.