Z
Zhenhua Dong
Researcher at Huawei
Publications - 98
Citations - 1237
Zhenhua Dong is an academic researcher from Huawei. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 11, co-authored 58 publications receiving 619 citations. Previous affiliations of Zhenhua Dong include Nankai University & College of Information Technology.
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Federated Meta-Learning with Fast Convergence and Efficient Communication
TL;DR: This work proposes a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches, and achieves a reduction in required communication cost and increase in accuracy as compared to Federated Averaging.
Proceedings ArticleDOI
WP:clubhouse?: an exploration of Wikipedia's gender imbalance
Shyong K. Lam,Anuradha Uduwage,Zhenhua Dong,Shilad Sen,David R. Musicant,Loren Terveen,John Riedl +6 more
TL;DR: A scientific exploration of the gender imbalance in the English Wikipedia's population of editors confirms the presence of a large gender gap among editors and a corresponding gender-oriented disparity in the content of Wikipedia's articles.
Proceedings ArticleDOI
A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data
TL;DR: This paper proposes a general knowledge distillation framework for counterfactual recommendation that enables uniform data modeling through four approaches that achieve better performance over the baseline models in terms of AUC and NLL.
Proceedings ArticleDOI
Improving Ad Click Prediction by Considering Non-displayed Events
TL;DR: This paper proposes a novel framework for counterfactual CTR prediction by considering not only displayed events but also non-displayed events and compares this framework against state-of-the-art conventional CTR models and existingcounterfactual learning approaches.
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DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction.
TL;DR: It is shown that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and the proposed framework, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.