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20182023
most citedMaking Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems

26 citations · 131 across the 18 of their papers we have counts for

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19 papers · 1 filter

cs.LG202326 cited

Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems

Yuyuan Li, Chaochao Chen, Xiaolin Zheng +4

With the growing privacy concerns in recommender systems, recommendation unlearning, i.e., forgetting the impact of specific learned targets, is getting increasing attention. Exist…

cs.LG2023

Integration of Large Language Models and Federated Learning

Chaochao Chen, Xiaohua Feng, Yuyuan Li +4

As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has…

cs.LG2023

Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation

Yuyuan Li, Jiaming Zhang, Yixiu Liu +1

Privacy concerns associated with machine learning models have driven research into machine unlearning, which aims to erase the memory of specific target training data from already…

cs.LG202011 cited

A Deep Framework for Cross-Domain and Cross-System Recommendations

Feng Zhu, Yan Wang, Chaochao Chen +3

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender system…

cs.LG20204 cited

Secret Sharing based Secure Regressions with Applications

Chaochao Chen, Liang Li, Wenjing Fang +6

Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potent…

cs.LG2020

Adapted tree boosting for Transfer Learning

Wenjing Fang, Chaochao Chen, Bowen Song +3

Secure online transaction is an essential task for e-commerce platforms. Alipay, one of the world's leading cashless payment platform, provides the payment service to both merchant…