# A Nonparametric Delayed Feedback Model for Conversion Rate Prediction

arXiv: https://arxiv.org/abs/1802.00255 · HTML: https://mlsanity.com/paper/1802.00255/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2018.

## Built on by (most cited first, 19 of 19)

- [Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling](https://mlsanity.com/paper/2104.14121/) (2021)
- [Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](https://mlsanity.com/paper/2202.06472/) (2022)
- [A Short Survey on Importance Weighting for Machine Learning](https://mlsanity.com/paper/2403.10175/) (2024)
- [Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems](https://mlsanity.com/paper/2206.00407/) (2022)
- [Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback](https://mlsanity.com/paper/2108.06167/) (2021)
- [Handling many conversions per click in modeling delayed feedback](https://mlsanity.com/paper/2101.02284/) (2021)
- [Ensure Timeliness and Accuracy: A Novel Sliding Window Data Stream Paradigm for Live Streaming Recommendation](https://mlsanity.com/paper/2402.14399/) (2024)
- [Biased Dueling Bandits with Stochastic Delayed Feedback](https://mlsanity.com/paper/2408.14603/) (2024)
- [Delayed Feedback Modeling for Post-Click Gross Merchandise Volume Prediction: Benchmark, Insights and Approaches](https://mlsanity.com/paper/2601.20307/) (2026)
- [Personalized Interpolation: Achieving Efficient Conversion Estimation with Flexible Optimization Windows](https://mlsanity.com/paper/2501.14103/) (2025)
- [Delayed Feedback Modeling with Influence Functions](https://mlsanity.com/paper/2502.01669/) (2025)
- [Multi-Head Online Learning for Delayed Feedback Modeling](https://mlsanity.com/paper/2205.12406/) (2022)
- [Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks](https://mlsanity.com/paper/2308.08071/) (2023)
- [Learning Concave Bid Shading Strategies in Online Auctions via Measure-valued Proximal Optimization](https://mlsanity.com/paper/2509.10693/) (2025)
- [Learning to Allocate Resources with Censored Feedback](https://mlsanity.com/paper/2602.06565/) (2026)
- [Linear Convergence in Games with Delayed Feedback via Extra Prediction](https://mlsanity.com/paper/2602.17486/) (2026)
- [Follow the TRACE: Exploiting Post-Click Trajectories for Online Delayed Conversion Rate Prediction](https://mlsanity.com/paper/2604.23197/) (2026)
- [TWICE: Two-Clock, Two-Window Learning for Long-Horizon Conversion Prediction in Online Advertising](https://mlsanity.com/paper/2607.25404/) (2026)
- [Multi-Objective Ranking for Live-Streaming: Balancing Fresh and Delayed Signals with Segment-Aware Targeting](https://mlsanity.com/paper/2608.04455/) (2026)

## Abstract (arXiv)

Predicting conversion rates (CVRs) in display advertising (e.g., predicting the proportion of users who purchase an item (i.e., a conversion) after its corresponding ad is clicked) is important when measuring the effects of ads shown to users and to understanding the interests of the users. There is generally a time delay (i.e., so-called it delayed feedback) between the ad click and conversion. Owing to the delayed feedback, samples that are converted after an observation period may be treated as negative. To overcome this drawback, CVR prediction assuming that the time delay follows an exponential distribution has been proposed. In practice, however, there is no guarantee that the delay is generated from the exponential distribution, and the best distribution with which to represent the delay depends on the data. In this paper, we propose a nonparametric delayed feedback model for CVR prediction that represents the distribution of the time delay without assuming a parametric distribution, such as an exponential or Weibull distribution. Because the distribution of the time delay is modeled depending on the content of an ad and the features of a user, various shapes of the distribution can be represented potentially. In experiments, we show that the proposed model can capture the distribution for the time delay on a synthetic dataset, even when the distribution is complicated. Moreover, on a real dataset, we show that the proposed model outperforms the existing method that assumes an exponential distribution for the time delay in terms of conversion rate prediction.