5 citations · 14 across the 5 of their papers we have counts for
5 papers
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…
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…
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
Yujing Zhang, Zhangming Chan, Shuhao Xu +4
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 dat…
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…
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
Pi Qi, Xiaoqiang Zhu, Guorui Zhou +5
Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online a…