4 papers
Probabilistic Residual Learning for Online Recommendations
Wenyuan Wang, Yusong Zhao, Zihao Xu +11
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffe…
Causal-Informed Hybrid Online Adaptive Optimization for Ad Load Personalization in Large-Scale Social Networks
Aakash Mishra, Qi Xu, Zhigang Hua +5
Personalizing ad load in large-scale social networks requires balancing user experience and conversions under operational constraints. Traditional primal-dual methods enforce const…
Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning
Tao Liu, Qi Xu, Wei Shi +2
Session-level dynamic ad load optimization aims to personalize the density and types of delivered advertisements in real time during a user's online session by dynamically balancin…
Ads Supply Personalization via Doubly Robust Learning
Wei Shi, Chen Fu, Qi Xu +5
Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-…