# Personalized Interpolation: Achieving Efficient Conversion Estimation with Flexible Optimization Windows

arXiv: https://arxiv.org/abs/2501.14103 · HTML: https://mlsanity.com/paper/2501.14103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [A Nonparametric Delayed Feedback Model for Conversion Rate Prediction](https://mlsanity.com/paper/1802.00255/) (2018)
- [Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling](https://mlsanity.com/paper/2012.03245/) (2020)
- [A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback](https://mlsanity.com/paper/2002.02068/) (2020)
- [Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](https://mlsanity.com/paper/2202.06472/) (2022)
- [Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising](https://mlsanity.com/paper/1305.3011/) (2013)
- [A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer](https://mlsanity.com/paper/1804.03280/) (2018)
- [Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction](https://mlsanity.com/paper/2011.11826/) (2020)
- [Learning Classifiers under Delayed Feedback with a Time Window Assumption](https://mlsanity.com/paper/2009.13092/) (2020)

## Built on by (most cited first, 1 of 1)

- [TWICE: Two-Clock, Two-Window Learning for Long-Horizon Conversion Prediction in Online Advertising](https://mlsanity.com/paper/2607.25404/) (2026)

## Abstract (arXiv)

Optimizing conversions is crucial in modern online advertising systems, enabling advertisers to deliver relevant products to users and drive business outcomes. However, accurately predicting conversion events remains challenging due to variable time delays between user interactions (e.g., impressions or clicks) and the actual conversions. These delays vary substantially across advertisers and products, necessitating flexible optimization windows tailored to specific conversion behaviors. To address this, we propose a novel textitPersonalized Interpolation method that extends existing models based on fixed conversion windows to support flexible advertiser-specific optimization windows. Our method enables accurate conversion estimation across diverse delay distributions without increasing system complexity. We evaluate the effectiveness of the proposed approach through extensive experiments using a real-world ads conversion model. Our results show that this method achieves both high prediction accuracy and improved efficiency compared to existing solutions. This study demonstrates the potential of our Personalized Interpolation method to improve conversion optimization and support a wider range of advertising strategies in large-scale online advertising systems.