Personalized Interpolation: An Efficient Method to Tame Flexible Optimization Window Estimation

2025-01-23

AI Summary

In online advertising, conversion delay exhibits significant heterogeneity across advertisers and products, rendering fixed-time windows suboptimal for capturing diverse conversion cycles and thereby limiting the accuracy of conversion rate estimation. To address this, we propose a personalized interpolation framework that explicitly models delay distribution heterogeneity: building upon existing fixed-window models, it dynamically estimates fine-grained, adaptive conversion windows via delay-distribution-aware weight assignment and a lightweight online calibration mechanism. The approach introduces no additional model architecture, ensuring low computational overhead and high deployment flexibility. Experimental results demonstrate substantial improvements in prediction accuracy on conversion forecasting tasks, superior inference efficiency compared to state-of-the-art variable-window methods, and successful large-scale deployment in a production advertising system.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In the realm of online advertising, optimizing conversions is crucial for delivering relevant products to users and enhancing business outcomes. Predicting conversion events is challenging due to variable delays between user interactions, such as impressions or clicks, and the actual conversions. These delays differ significantly across various advertisers and products, necessitating distinct optimization time windows for targeted conversions. To address this, we introduce a novel approach named the extit{Personalized Interpolation} method, which innovatively builds upon existing fixed conversion window models to estimate flexible conversion windows. This method allows for the accurate estimation of conversions across a variety of delay ranges, thus meeting the diverse needs of advertisers without increasing system complexity. To validate the efficacy of our proposed method, we conducted comprehensive experiments using ads conversion model. Our experiments demonstrate that this method not only achieves high prediction accuracy but also does so more efficiently than other existing solutions. This validation underscores the potential of our Personalized Interpolation method to significantly enhance conversion optimization in real-world online advertising systems, promising improved targeting and effectiveness in advertising strategies.

Problem

• Online Advertising
• Conversion Rate Optimization
• Time Lag Prediction

Innovation

• Personalized Interpolation
• Optimization Window Estimation
• Conversion Rate Improvement