16 citations · 28 across the 12 of their papers we have counts for
12 papers
See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction
Sishuo Chen, Zhangming Chan, Xiang-Rong Sheng +6
Conversion rate (CVR) prediction is a core component of online advertising systems, where the attribution mechanisms-rules for allocating conversion credit across user touchpoints-…
Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights
Xiang-Rong Sheng, Feifan Yang, Litong Gong +10
Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, pred…
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
Xiaoqiang Gui, Yueyao Cheng, Xiang-Rong Sheng +6
In machine learning systems, privileged features refer to the features that are available during offline training but inaccessible for online serving. Previous studies have recogni…
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Yunfeng Zhao, Xu Yan, Xiaoqiang Gui +6
Conversion rate (CVR) prediction is an essential task for large-scale e-commerce platforms. However, refund behaviors frequently occur after conversion in online shopping systems,…
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach
Zhangming Chan, Yu Zhang, Shuguang Han +8
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-calibrated CVR e…
COPR: Consistency-Oriented Pre-Ranking for Online Advertising
Zhishan Zhao, Jingyue Gao, Yu Zhang +8
Cascading architecture has been widely adopted in large-scale advertising systems to balance efficiency and effectiveness. In this architecture, the pre-ranking model is expected t…
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
Zhao-Yu Zhang, Xiang-Rong Sheng, Yujing Zhang +4
Deep learning techniques have been applied widely in industrial recommendation systems. However, far less attention has been paid to the overfitting problem of models in recommenda…
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
Xiang-Rong Sheng, Jingyue Gao, Yueyao Cheng +6
Despite the development of ranking optimization techniques, pointwise loss remains the dominating approach for click-through rate prediction. It can be attributed to the calibratio…
On Ranking Consistency of Pre-ranking Stage
Siyu Gu, Xiangrong Sheng
Industrial ranking systems, such as advertising systems, rank items by aggregating multiple objectives into one final objective to satisfy user demand and commercial intent. Cascad…
Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling
Siyu Gu, Xiang-Rong Sheng, Ying Fan +2
One of the difficulties of conversion rate (CVR) prediction is that the conversions can delay and take place long after the clicks. The delayed feedback poses a challenge: fresh da…
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou +8
Traditional industrial recommenders are usually trained on a single business domain and then serve for this domain. However, in large commercial platforms, it is often the case tha…
CAN: Feature Co-Action for Click-Through Rate Prediction
Weijie Bian, Kailun Wu, Lejian Ren +12
Feature interaction has been recognized as an important problem in machine learning, which is also very essential for click-through rate (CTR) prediction tasks. In recent years, De…