2 citations · 3 across the 5 of their papers we have counts for
5 papers
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning
Penghui Wei, Hongjian Dou, Shaoguo Liu +4
Conversion rate (CVR) estimation aims to predict the probability of conversion event after a user has clicked an ad. Typically, online publisher has user browsing interests and cli…
Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning
Xuanhua Yang, Jianxin Zhao, Shaoguo Liu +2
Multi-task learning (MTL) has been widely applied in online advertising and recommender systems. To address the negative transfer issue, recent studies have proposed optimization m…
Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking
Shanlei Mu, Penghui Wei, Wayne Xin Zhao +3
Multi-scenario ad ranking aims at leveraging the data from multiple domains or channels for training a unified ranking model to improve the performance at each individual scenario.…
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
Xuanhua Yang, Xiaoyu Peng, Penghui Wei +3
Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple…
MCMF: Multi-Constraints With Merging Features Bid Optimization in Online Display Advertising
Xiao Wang, Shaoguo Liu, Yidong Jia +4
In the Real-Time Bidding (RTB), advertisers are increasingly relying on bid optimization to gain more conversions (i.e trade or arrival). Currently, the efficiency of bid optimizat…