Addressing Delayed Feedback in Conversion Rate Prediction via Influence Functions

• URL: http://arxiv.org/abs/2502.01669v1
• Date: Sat, 01 Feb 2025 16:23:13 GMT
• Title: Addressing Delayed Feedback in Conversion Rate Prediction via Influence Functions
• Authors: Chenlu Ding, Jiancan Wu, Yancheng Yuan, Junfeng Fang, Cunchun Li, Xiang Wang, Xiangnan He,
• Abstract summary: We propose an Influence Function-empowered framework for Delayed Feedback Modeling (IF-DFM) IF-DFM leverages influence functions to estimate how newly acquired and delayed conversion data impact model parameters. Experiments on benchmark datasets demonstrate that IF-DFM consistently surpasses state-of-the-art methods.
• Score: 23.97164200705282
• License:
• Abstract: In the realm of online digital advertising, conversion rate (CVR) prediction plays a pivotal role in maximizing revenue under cost-per-conversion (CPA) models, where advertisers are charged only when users complete specific actions, such as making a purchase. A major challenge in CVR prediction lies in the delayed feedback problem-conversions may occur hours or even weeks after initial user interactions. This delay complicates model training, as recent data may be incomplete, leading to biases and diminished performance. Although existing methods attempt to address this issue, they often fall short in adapting to evolving user behaviors and depend on auxiliary models, which introduces computational inefficiencies and the risk of model inconsistency. In this work, we propose an Influence Function-empowered framework for Delayed Feedback Modeling (IF-DFM). IF-DFM leverages influence functions to estimate how newly acquired and delayed conversion data impact model parameters, enabling efficient parameter updates without the need for full retraining. Additionally, we present a scalable algorithm that efficiently computes parameter updates by reframing the inverse Hessian-vector product as an optimization problem, striking a balance between computational efficiency and effectiveness. Extensive experiments on benchmark datasets demonstrate that IF-DFM consistently surpasses state-of-the-art methods, significantly enhancing both prediction accuracy and model adaptability.

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