Publications (18)
MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms
Jinqi Wu, Sishuo Chen, Zhangming Chan +9
Multi-attribution learning (MAL), which enhances model performance by learning from conversion labels yielded by multiple attribution mechanisms, has emerged as a promising learnin…
MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling
Bin Wu, Feifan Yang, Zhangming Chan +8
Lifelong user interest modeling is crucial for industrial recommender systems, yet existing approaches rely predominantly on ID-based features, suffering from poor generalization o…
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-…
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…
AIF: Asynchronous Inference Framework for Cost-Effective Pre-Ranking
Zhi Kou, Xiang-Rong Sheng, Shuguang Han +5
In industrial recommendation systems, pre-ranking models based on deep neural networks (DNNs) commonly adopt a sequential execution framework: feature fetching and model forward co…
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…
Modeling Cascaded Delay Feedback for Online Net Conversion Rate Prediction: Benchmark, Insights and Solutions
Mingxuan Luo, Guipeng Xv, Sishuo Chen +8
In industrial recommender systems, conversion rate (CVR) is widely used for traffic allocation, but it fails to fully reflect recommendation effectiveness because it ignores refund…
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…
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,…
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…
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…
Fine-grained Semantics Integration for Large Language Model-based Recommendation
Jiawei Feng, Xiaoyu Kong, Leheng Sheng +8
Recent advances in Large Language Models (LLMs) have driven a shift in recommender systems from the discriminative paradigm to the LLM-based generative paradigm, where the recommen…
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…
EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling
Mingyang Liu, Yong Bai, Zhangming Chan +5
Efficiently scaling industrial Click-Through Rate (CTR) prediction has recently attracted significant research attention. Existing approaches typically employ early aggregation of…
Delayed Feedback Modeling for Post-Click Gross Merchandise Volume Prediction: Benchmark, Insights and Approaches
Xinyu Li, Sishuo Chen, Guipeng Xv +7
The prediction objectives of online advertisement ranking models are evolving from probabilistic metrics like conversion rate (CVR) to numerical business metrics like post-click gr…
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…
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…
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…