A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback
Using importance weighting to correct feedback shift under delayed feedback, enhancing conversion rate prediction accuracy.
Key Findings
Methodology
This paper proposes an importance weighting approach to address feedback shift under delayed feedback. By estimating the conditional label distribution differences between training and test data, it corrects the loss function during training. The method is theoretically proven to be consistent and validated through offline and online experiments.
Key Results
- On the Criteo dataset, the model using importance weighting improved log loss by 1.5% and normalized log loss by 2.5% compared to existing methods.
- In the Dynalyst in-house dataset, the FFMIW method significantly outperformed FFM in Campaign L, especially in normalized log loss.
- Online A/B tests showed that campaigns using the FFMIW method performed better than traditional methods.
Significance
This research is significant in the field of display advertising, addressing the issue of label distribution inconsistency between training data and production environment caused by delayed feedback. By improving conversion rate prediction accuracy, advertisers can more accurately assess ad value and optimize ad placement strategies.
Technical Contribution
The technical contribution lies in proposing a new feedback shift correction method, extending the application of importance weighting to delayed feedback problems. Compared to existing methods, this approach offers new theoretical guarantees and enables more efficient model training.
Novelty
This is the first application of importance weighting for feedback shift correction under delayed feedback. Unlike previous methods assuming exponential delay distribution, this approach captures periodic variations in delay distribution.
Limitations
- The method relies on a hypothesized counterfactual deadline, which if improperly set, may lead to estimation bias.
- The model may be less sensitive to changes in background CVR on certain datasets.
- Further research is needed for application in more complex advertising environments.
Future Work
Future research directions include exploring more complex delay distribution models, further optimizing importance weight estimation methods, and application and validation across different advertising platforms.
AI Executive Summary
In display advertising, accurately predicting conversion rates is crucial for assessing the value of ad displays. However, due to the time delay between clicks and conversions, positive instances in training data may be incorrectly labeled as negative, leading to label distribution inconsistency between training data and production environment. This paper proposes an importance weighting approach to address this feedback shift issue. By estimating the conditional label distribution differences between training and test data, it corrects the loss function during training. Experimental results show that this method outperforms existing methods on both Criteo and Dynalyst datasets, particularly in log loss and normalized log loss. This research not only provides new theoretical guarantees but also achieves more efficient model training, holding significant academic and practical application value. Despite potential limitations under certain assumptions, it offers new directions for future research.
Deep Analysis
Background
With the rise of programmatic ad buying, real-time bidding has become the norm for ad placement. Advertisers often prefer the cost-per-acquisition model as it is more likely to yield profits. However, the time delay between clicks and conversions leads to label distribution inconsistency in training data, affecting conversion rate prediction accuracy.
Core Problem
Delayed feedback causes positive instances in training data to be mislabeled as negative, creating a feedback shift problem. This situation results in different conditional label distributions between training and test data, impacting model prediction performance.
Innovation
This paper innovatively applies importance weighting to feedback shift correction. By estimating the conditional label distribution differences between training and test data, it corrects the loss function during training, improving model consistency and accuracy.
Methodology
- �� Correct feedback shift using importance weighting.
- �� Estimate conditional label distribution differences between training and test data.
- �� Use LightGBM for importance weight estimation.
- �� Validate through experiments on Criteo and Dynalyst datasets.
Experiments
On the Criteo dataset, a 7-day counterfactual deadline was used for importance weight estimation. The experimental design included evaluations of log loss, PR-AUC, and normalized log loss. Online A/B tests further validated the method's effectiveness.
Results
On the Criteo dataset, the model using importance weighting improved log loss by 1.5% and normalized log loss by 2.5%. On the Dynalyst dataset, the FFMIW method significantly outperformed FFM in Campaign L.
Applications
This method can be directly applied to conversion rate prediction in display advertising, helping advertisers optimize ad placement strategies and improve return on investment.
Limitations & Outlook
The method relies on a hypothesized counterfactual deadline, which if improperly set, may lead to estimation bias. Additionally, the model may be less sensitive to changes in background CVR on certain datasets.
Plain Language Accessible to non-experts
Imagine you're working in a restaurant, and customers take time to finish their meals. You need to predict if they'll be satisfied, but some haven't finished yet, so you base your judgment on those who have. To be more accurate, you adjust your predictions based on feedback. This process is like the method in this paper, adjusting weights to better predict conversion rates.
ELI14 Explained like you're 14
Imagine you're playing a game where you have to predict if your friends will complete a challenge in the next few days. The problem is, some friends might take longer. You need to adjust your predictions based on data from friends who have already finished, just like this paper's method, which adjusts weights to improve prediction accuracy.
Glossary
Delayed Feedback
Refers to the time delay between ad clicks and conversions, causing potential inaccuracies in training data labels.
In this paper, delayed feedback leads to label distribution inconsistency between training and test data.
Conversion Rate
The probability that a user takes a predefined action after clicking an ad, such as purchasing a product.
Conversion rate is a key metric for assessing ad display value.
Importance Weight
Weights used to correct distribution differences between training and test data.
This paper uses importance weights to correct feedback shift issues.
Feedback Shift
A situation where conditional label distributions differ between training and test data.
The proposed method addresses feedback shift problems.
Field-aware Factorization Machines
A machine learning model for handling high-dimensional sparse data.
FFM is used to evaluate the effectiveness of the importance weighting method in experiments.
Open Questions Unanswered questions from this research
- 1 How to apply importance weighting in more complex advertising environments remains to be explored.
- 2 Research is needed on optimizing counterfactual deadline settings to improve estimation accuracy.
- 3 Application and validation across different advertising platforms require further study.
Applications
Immediate Applications
Ad Placement Optimization
Advertisers can use this method to improve conversion rate prediction accuracy, optimizing ad placement strategies and increasing return on investment.
Long-term Vision
Advertising Industry Transformation
This method has the potential to change ad placement strategies in the industry, improving overall ad effectiveness through more accurate predictions.
Abstract
In display advertising, predicting the conversion rate, that is, the probability that a user takes a predefined action on an advertiser's website, such as purchasing goods is fundamental in estimating the value of displaying the advertisement. However, there is a relatively long time delay between a click and its resultant conversion. Because of the delayed feedback, some positive instances at the training period are labeled as negative because some conversions have not yet occurred when training data are gathered. As a result, the conditional label distributions differ between the training data and the production environment. This situation is referred to as a feedback shift. We address this problem by using an importance weight approach typically used for covariate shift correction. We prove its consistency for the feedback shift. Results in both offline and online experiments show that our proposed method outperforms the existing method.