26 citations · 43 across the 7 of their papers we have counts for
7 papers
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
Xinting Liao, Weiming Liu, Chaochao Chen +7
Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised…
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation
Jiajie Su, Chaochao Chen, Zibin Lin +3
Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interacti…
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…
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
Xinting Liao, Chaochao Chen, Weiming Liu +7
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective i…
Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks
Xiaolin Zheng, Zhongyu Wang, Chaochao Chen +2
The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users' conversions are always delayed in online commercial systems. Alth…