Towards understanding the overfitting phenomenon of deep click-through rate models
Deep learning techniques have been applied widely in industrial recommendation systems.
However, far less attention has been paid on the overfitting problem of models in …
However, far less attention has been paid on the overfitting problem of models in …
KEEP: An industrial pre-training framework for online recommendation via knowledge extraction and plugging
An industrial recommender system generally presents a hybrid list that contains results from
multiple subsystems. In practice, each subsystem is optimized with its own feedback data to …
multiple subsystems. In practice, each subsystem is optimized with its own feedback data to …
Cross-domain recommendation via adversarial adaptation
Data scarcity, eg, labeled data being either unavailable or too expensive, is a perpetual
challenge of recommendation systems. Cross-domain recommendation leverages the label …
challenge of recommendation systems. Cross-domain recommendation leverages the label …
Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
Conversion rate prediction is critical to many online applications such as digital display
advertising. To capture dynamic data distribution, industrial systems often require retraining …
advertising. To capture dynamic data distribution, industrial systems often require retraining …
Joint optimization of ranking and calibration with contextualized hybrid model
Despite the development of ranking optimization techniques, pointwise loss remains the
dominating approach for click-through rate prediction. It can be attributed to the calibration …
dominating approach for click-through rate prediction. It can be attributed to the calibration …
Capturing conversion rate fluctuation during sales promotions: A novel historical data reuse approach
Conversion rate (CVR) prediction is one of the core components in online recommender
systems, and various approaches have been proposed to obtain accurate and well …
systems, and various approaches have been proposed to obtain accurate and well …
Asymptotically unbiased estimation for delayed feedback modeling via label correction
Y Chen, J Jin, H Zhao, P Wang, G Liu, J Xu… - Proceedings of the ACM …, 2022 - dl.acm.org
Alleviating the delayed feedback problem is of crucial importance for the conversion rate
(CVR) prediction in online advertising. Previous delayed feedback modeling methods using …
(CVR) prediction in online advertising. Previous delayed feedback modeling methods using …
Generalized delayed feedback model with post-click information in recommender systems
Predicting conversion rate (eg, the probability that a user will purchase an item) is a
fundamental problem in machine learning based recommender systems. However, accurate …
fundamental problem in machine learning based recommender systems. However, accurate …
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Conversion rate (CVR) prediction is an essential task for e-commerce platforms. However,
refunds frequently occur after conversion in online shopping systems, which drives us to pay …
refunds frequently occur after conversion in online shopping systems, which drives us to pay …
Online Conversion Rate Prediction via Neural Satellite Networks in Delayed Feedback Advertising
Q Liu, H Li, X Ao, Y Guo, Z Dong, R Zhang… - Proceedings of the 46th …, 2023 - dl.acm.org
The delayed feedback is becoming one of the main obstacles in online advertising due to
the pervasive deployment of the cost-per-conversion display strategy requesting a real-time …
the pervasive deployment of the cost-per-conversion display strategy requesting a real-time …