Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and Blockchain
Recommender systems (RS) play an integral role in many online platforms. Exponential
growth and potential commercial interests are raising significant concerns around privacy …
growth and potential commercial interests are raising significant concerns around privacy …
Horizontal Federated Recommender System: A Survey
Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …
privacy leakage exists in the centralized-training recommender system (RecSys). To this …
Fedgr: Cross-platform federated group recommendation system with hypergraph neural networks
J Zeng, Z Huang, Z Wu, Z Chen, Y Chen - Journal of Intelligent Information …, 2024 - Springer
Group recommendation systems are widely applied in social media, e-commerce, and
diverse platforms. These systems face challenges associated with data privacy constraints …
diverse platforms. These systems face challenges associated with data privacy constraints …
HN3S: A federated autoencoder framework for collaborative filtering via hybrid negative sampling and secret sharing
L Zhang, G Li, L Yuan, X Ding, Q Rong - Information Processing & …, 2024 - Elsevier
Federated recommender systems can serve users with suitable item recommendations
while preserving their privacy, but most current works cannot serve non-participant users …
while preserving their privacy, but most current works cannot serve non-participant users …
Hybrid Learning: When Centralized Learning Meets Federated Learning in the Mobile Edge Computing Systems
Federated learning is a new artificial intelligence technology with which an edge server can
orchestrate with multiple end users to train a global model collaboratively. Under this setting …
orchestrate with multiple end users to train a global model collaboratively. Under this setting …
EFVAE: Efficient Federated Variational Autoencoder for Collaborative Filtering
L Zhang, Q Rong, X Ding, G Li, L Yuan - Proceedings of the 33rd ACM …, 2024 - dl.acm.org
Federated recommender systems are used to address privacy issues in recommendations.
Among them, FedVAE extends the representative non-linear recommendation method …
Among them, FedVAE extends the representative non-linear recommendation method …
Resource Management and Optimization in Internet of Vehicles for Hierarchical Federated Learning
T Yuan, L Chen, Y Jiang, H Chen, W Gong, Y Gu - IEEE Access, 2024 - ieeexplore.ieee.org
Due to limited network resources in internet of vehicles (IoV), vehicle's heterogenous data,
communication and computing resources significantly impact the training delay and model …
communication and computing resources significantly impact the training delay and model …
Privacy-Preserving Mobility-Aware Federated Collaborative Filtering Framework for Caching Prediction in Vehicular Networks
Recommendation algorithm can effectively reduce the difficulty of proactive edge caching
prediction by excavating users' preferences among the massive contents, which has drawn …
prediction by excavating users' preferences among the massive contents, which has drawn …
Research on Collaborative Filtering Recommendation Algorithm Based on Fuzzy Clustering
Y Wang - 2023 3rd International Conference on Smart …, 2023 - ieeexplore.ieee.org
In e-commerce, it is very important to understand the preferences of users to better
personalize services. Therefore, the recommendation system is particularly important, and …
personalize services. Therefore, the recommendation system is particularly important, and …