Maoguo Gong

• Towards Adaptive Personalized Federated Meta-Learning Recommendation

  Open Access•Shanfeng Wang, Shanyang Gao et al.•ARTICLE•IEEE Transactions on Computational…•2026

  Incluir item

  Federated recommendation systems aim to provide high-quality recommendations while protecting user privacy. However, existing federated recommendation algorithms typically use a unified item embedding framework, where all clients share the same item representations. Such approaches often fail to capture users’ personalized perceptions of the same item and struggle to reflect subtle changes in user preferences, limiting the effectiveness of person…

• Multi-Objective Neural Architecture Search for Cognitive Diagnosis

  Open Access•Yifei Sun, Mingkai Duan et al.•ARTICLE•IEEE Transactions on Computational…•2026

  Incluir item

  Cognitive diagnosis (CD) is a fundamental task within computational social systems, essential for personalizing learning in intelligent education by assessing a learner’s fine-grained knowledge proficiency. A critical dilemma exists in designing cognitive diagnosis models (CDMs): conventional psychometric models are interpretable but often simplistic, while complex deep learning models achieve high accuracy at the cost of becoming uninterpretable…

• Personalized Federated Contrastive Learning for Recommendation

  Open Access•Shanfeng Wang, Yuxi Zhou et al.•ARTICLE•IEEE Transactions on Computational…•2025

  Incluir item

  Recommender systems play crucial roles in addressing the issue of information overload, but traditional centralized storage in recommendation poses significant privacy concerns. In recent years, federated learning has been successfully introduced into a recommendation, while these algorithms still encounter several challenges. First, real-world recommendation scenarios often suffer from sparse data, making it difficult for models to learn reliabl…

• Privacy-Preserving Global Structural Balance Computation in Signed Networks

  Open Access•Lijia Ma, Xiaopeng Huang et al.•ARTICLE•IEEE Transactions on Computational…•2020

  Incluir item

  The studies on signed networks have received a great attention due to their capabilities on presenting conflicting relationships, which reflect the potential conflicts and tensions of complex systems. To further understand those conflicts and tensions, many methods have been proposed for computing the global structural balance (GSB) of signed networks, which aim to discover the most balanced state of the networks with the least number of unbalanc…

• Optimizing dynamical changes of structural balance in signed network based on memetic algorithm

  Open Access•Shanfeng Wang, Maoguo Gong et al.•ARTICLE•Social Networks•2016

  Incluir item

• Optimizing dynamical changes of structural balance in signed network based on memetic algorithm

  Open Access•Shanfeng Wang, Maoguo Gong et al.•ARTICLE•Social Networks•2016

  Incluir item

• Privacy-Preserving Global Structural Balance Computation in Signed Networks

  Open Access•Lijia Ma, Xiaopeng Huang et al.•ARTICLE•IEEE Transactions on Computational…•2020

  Incluir item

  The studies on signed networks have received a great attention due to their capabilities on presenting conflicting relationships, which reflect the potential conflicts and tensions of complex systems. To further understand those conflicts and tensions, many methods have been proposed for computing the global structural balance (GSB) of signed networks, which aim to discover the most balanced state of the networks with the least number of unbalanc…

• Personalized Federated Contrastive Learning for Recommendation

  Open Access•Shanfeng Wang, Yuxi Zhou et al.•ARTICLE•IEEE Transactions on Computational…•2025

  Incluir item

  Recommender systems play crucial roles in addressing the issue of information overload, but traditional centralized storage in recommendation poses significant privacy concerns. In recent years, federated learning has been successfully introduced into a recommendation, while these algorithms still encounter several challenges. First, real-world recommendation scenarios often suffer from sparse data, making it difficult for models to learn reliabl…

• Towards Adaptive Personalized Federated Meta-Learning Recommendation

  Open Access•Shanfeng Wang, Shanyang Gao et al.•ARTICLE•IEEE Transactions on Computational…•2026

  Incluir item

  Federated recommendation systems aim to provide high-quality recommendations while protecting user privacy. However, existing federated recommendation algorithms typically use a unified item embedding framework, where all clients share the same item representations. Such approaches often fail to capture users’ personalized perceptions of the same item and struggle to reflect subtle changes in user preferences, limiting the effectiveness of person…

• Multi-Objective Neural Architecture Search for Cognitive Diagnosis

  Open Access•Yifei Sun, Mingkai Duan et al.•ARTICLE•IEEE Transactions on Computational…•2026

  Incluir item

  Cognitive diagnosis (CD) is a fundamental task within computational social systems, essential for personalizing learning in intelligent education by assessing a learner’s fine-grained knowledge proficiency. A critical dilemma exists in designing cognitive diagnosis models (CDMs): conventional psychometric models are interpretable but often simplistic, while complex deep learning models achieve high accuracy at the cost of becoming uninterpretable…

Artificial Intelligence (3 obras) · Computer Science (3 obras) · Algorithm (2 obras) · Complex Network Analysis Techniques (2 obras) · Privacy-Preserving Technologies in Data (2 obras) · Recommender Systems and Techniques (2 obras) · Statistical model (2 obras) · Advanced Technologies in Various Fields (1 obra) · AI-based Problem Solving and Planning (1 obra) · Artificial neural network (1 obra)