47 citations · 314 across the 34 of their papers we have counts for
7 papers · 1 filter
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…
In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
Zhongxuan Han, Chaochao Chen, Xiaolin Zheng +4
Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The ex…
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…
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…
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning
Yuyuan Li, Chaochao Chen, Xiaolin Zheng +3
Recent regulations on the Right to be Forgotten have greatly influenced the way of running a recommender system, because users now have the right to withdraw their private data. Be…
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
Feng Zhu, Mingjie Zhong, Xinxing Yang +8
In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-…
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
Chuanpan Zheng, Xiaoliang Fan, Cheng Wang +3
Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with…