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Yingying Zhao
Researcher at Fudan University
Publications - 25
Citations - 365
Yingying Zhao is an academic researcher from Fudan University. The author has contributed to research in topics: Computer science & Collaborative filtering. The author has an hindex of 6, co-authored 19 publications receiving 232 citations. Previous affiliations of Yingying Zhao include Tongji University.
Papers
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Journal ArticleDOI
An algorithm for efficient privacy-preserving item-based collaborative filtering
TL;DR: An efficient privacy-preserving item-based collaborative filtering algorithm is proposed, which can protect user privacy during online recommendation process without compromising recommendation accuracy and efficiency.
Journal ArticleDOI
Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data
TL;DR: Wang et al. as discussed by the authors presented a solution for predictive maintenance of wind turbine generators, which can improve wind turbine reliability and prolong operation time, thereby reducing the O&M cost for wind power plants.
Proceedings ArticleDOI
WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation
TL;DR: WEMAREC is presented, a weighted and ensemble matrix approximation method for accurate and scalable recommendation that builds upon the intuition that (sub)matrices containing more frequent samples of certain user/ item/rating tend to make more reliable rating predictions for these specific user/item/rating.
Journal ArticleDOI
Hierarchical Anomaly Detection and Multimodal Classification in Large-Scale Photovoltaic Systems
TL;DR: This paper presents a data-driven anomaly detection and classification solution, which can accurately detect and classify diverse photovoltaic system anomalies and has been deployed in two large-scale solar farms.
Proceedings ArticleDOI
Fault prognosis of wind turbine generator using SCADA data
TL;DR: A prognosis method to predict the remaining useful life (RUL) of generators, which requires no additional hardware support beyond widely adopted SCADA system, and quantitatively measure wind turbine performance degradation in runtime is presented.