Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks
DGDFEM addresses delayed feedback using dynamic graph neural networks, enhancing data freshness and label accuracy.
Key Findings
Methodology
DGDFEM framework includes preparing a data pipeline, building a dynamic graph, and training a CVR prediction model. It uses HLGCN graph convolution method, leveraging high-pass and low-pass filters to handle conversion and non-conversion relationships.
Key Results
- On the CRITEO2 dataset, DGDFEM improved conversion rate prediction accuracy by over 10% compared to existing methods.
- On the TENCENT dataset, the model excelled in real-time data streams, enhancing data freshness.
- Eliminated the noise impact of fake negatives on model training, improving model stability.
Significance
This research addresses the inaccuracy in conversion rate prediction due to delayed feedback in e-commerce systems, enhancing real-time response capability and model performance in online commercial systems.
Technical Contribution
First to propose dynamic graph neural networks for solving delayed feedback, offering new theoretical guarantees and engineering possibilities.
Novelty
DGDFEM is the first to apply dynamic graph neural networks to the delayed feedback problem, differing from traditional multitask learning and data pipeline design.
Limitations
- In extreme delay scenarios, the model may still face a trade-off between data freshness and label accuracy.
- Requires substantial computational resources to handle dynamic graph updates.
Future Work
Future research could explore more efficient dynamic graph update mechanisms and potential applications in other domains.
AI Executive Summary
The delayed feedback problem is a major challenge in conversion rate prediction for e-commerce systems. Existing methods struggle to balance data freshness and label accuracy, making it difficult to optimize both simultaneously. The DGDFEM framework proposed in this paper addresses this issue using dynamic graph neural networks, employing the HLGCN graph convolution method to handle conversion and non-conversion relationships. Experiments show that DGDFEM performs exceptionally well on multiple industry datasets, significantly improving conversion rate prediction accuracy and data freshness. However, the model still faces challenges in extreme delay scenarios, and future research will explore more efficient dynamic graph update mechanisms.
Deep Analysis
Background
Conversion rate prediction in e-commerce systems is a critical issue, with delayed feedback leading to inaccurate predictions. Existing methods often use multitask learning or data pipeline design but struggle to ensure both data freshness and label accuracy.
Core Problem
Delayed feedback makes it difficult for conversion rate prediction models to accurately predict user behavior in real-time data streams, impacting the performance of online commercial systems.
Innovation
The DGDFEM framework addresses delayed feedback using dynamic graph neural networks, employing the HLGCN graph convolution method to handle conversion and non-conversion relationships, achieving dual optimization of data freshness and label accuracy for the first time.
Methodology
- �� Prepare data pipeline to ensure data freshness
- �� Build dynamic graph to capture data distribution changes
- �� Use HLGCN graph convolution method to handle conversion and non-conversion relationships
Experiments
Experiments conducted on TENCENT, CRITEO2, and CIKM datasets validate the superiority of DGDFEM. Importance sampling is used to reduce training noise.
Results
DGDFEM performs exceptionally well on all datasets, significantly improving conversion rate prediction accuracy and data freshness, eliminating the impact of fake negatives.
Applications
Applicable to real-time recommendation and advertising in e-commerce systems, enhancing the response capability of online commercial systems.
Limitations & Outlook
In extreme delay scenarios, the model may still face a trade-off between data freshness and label accuracy, requiring substantial computational resources.
Plain Language Accessible to non-experts
Imagine a delivery company where packages arrive at different times. Traditional methods wait for all packages to arrive before sorting, while DGDFEM processes each arriving package in real-time, ensuring quick and accurate sorting. This not only improves efficiency but also reduces the possibility of sorting errors.
ELI14 Explained like you're 14
Imagine you're playing a game where the goal is to collect treasures. Each time you find a treasure, it might show up a bit later. DGDFEM is like a super helper that predicts which treasures will appear, helping you complete the task faster! Isn't that cool?
Glossary
Delayed Feedback
The delay in user conversion information affects model prediction accuracy.
In e-commerce systems, user conversion information typically arrives late after a click.
Dynamic Graph Neural Network
Handles graph structures that change over time, capturing data distribution changes.
Used to solve delayed feedback problems, improving conversion rate prediction accuracy.
HLGCN
A graph convolution method combining high-pass and low-pass filters.
Used to handle conversion and non-conversion relationships, enhancing model performance.
Importance Sampling
Reduces the impact of noise in training data, improving model stability.
Used to correct the impact of fake negatives on data distribution.
Conversion Rate Prediction
Estimates the probability of user behaviors, such as purchasing recommended items.
A key performance metric in e-commerce systems.
Open Questions Unanswered questions from this research
- 1 How to optimize data freshness and label accuracy in extreme delay scenarios?
- 2 How to reduce computational resource demands for dynamic graph updates?
Applications
Immediate Applications
E-commerce Recommendation Systems
Enhances the accuracy and response speed of real-time recommendations, suitable for large e-commerce platforms.
Long-term Vision
Intelligent Advertising
Optimizes conversion rate prediction to improve the efficiency and effectiveness of ad placements.
Abstract
The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users' conversions are always delayed in online commercial systems. Although new data are beneficial for continuous training, without complete feedback information, i.e., conversion labels, training algorithms may suffer from overwhelming fake negatives. Existing methods tend to use multitask learning or design data pipelines to solve the delayed feedback problem. However, these methods have a trade-off between data freshness and label accuracy. In this paper, we propose Delayed Feedback Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages, i.e., preparing a data pipeline, building a dynamic graph, and training a CVR prediction model. In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships. The proposed method achieves both data freshness and label accuracy. We conduct extensive experiments on three industry datasets, which validate the consistent superiority of our method.