61 citations · 85 across the 7 of their papers we have counts for
7 papers
End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling
Zexu Sun, Hao Yang, Dugang Liu +3
In modern online platforms, incentives are essential factors that enhance user engagement and increase platform revenue. Over recent years, uplift modeling has been introduced as a…
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Shiwei Li, Wenchao Xu, Haozhao Wang +7
Federated learning is a promising distributed machine learning paradigm that can effectively exploit large-scale data without exposing users' privacy. However, it may incur signifi…
Embedding Compression in Recommender Systems: A Survey
Shiwei Li, Huifeng Guo, Xing Tang +4
To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information filtering services. Usually, embedding tables are emp…
Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
Bowei He, Yunpeng Weng, Xing Tang +5
Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individ…
RecDCL: Dual Contrastive Learning for Recommendation
Dan Zhang, Yangliao Geng, Wenwen Gong +6
Self-supervised learning (SSL) has recently achieved great success in mining the user-item interactions for collaborative filtering. As a major paradigm, contrastive learning (CL)…
Towards Automated Negative Sampling in Implicit Recommendation
Fuyuan Lyu, Yaochen Hu, Xing Tang +3
Negative sampling methods are vital in implicit recommendation models as they allow us to obtain negative instances from massive unlabeled data. Most existing approaches focus on s…