Recent advances and future challenges in federated recommender systems
Recommender systems are an integral part of modern-day user experience. They
understand their preferences and support them in discovering meaningful content by …
understand their preferences and support them in discovering meaningful content by …
Swarm intelligence techniques in recommender systems-A review of recent research
One of the main current applications of Intelligent Systems are Recommender systems (RS).
RS can help users to find relevant items in huge information spaces in a personalized way …
RS can help users to find relevant items in huge information spaces in a personalized way …
Machine learning dismantling and early-warning signals of disintegration in complex systems
From physics to engineering, biology and social science, natural and artificial systems are
characterized by interconnected topologies whose features–eg, heterogeneous connectivity …
characterized by interconnected topologies whose features–eg, heterogeneous connectivity …
A probabilistic model for using social networks in personalized item recommendation
Preference-based recommendation systems have transformed how we consume media. By
analyzing usage data, these methods uncover our latent preferences for items (such as …
analyzing usage data, these methods uncover our latent preferences for items (such as …
Meta matrix factorization for federated rating predictions
With distinct privacy protection advantages, federated recommendation is becoming
increasingly feasible to store data locally in devices and federally train recommender …
increasingly feasible to store data locally in devices and federally train recommender …
HeteroGraphRec: A heterogeneous graph-based neural networks for social recommendations
Recommender systems in social networks are widely used for connecting users to their
desired items from a vast catalog of available items. Learning the user's preferences from all …
desired items from a vast catalog of available items. Learning the user's preferences from all …
A medical big data access control model based on fuzzy trust prediction and regression analysis
R Jiang, Y **n, Z Chen, Y Zhang - Applied Soft Computing, 2022 - Elsevier
One of the important issues facing HIS (Hospital Information System) in the context of big
data is how to ensure that massive data and resources are protected from internal attacks …
data is how to ensure that massive data and resources are protected from internal attacks …
Social network-driven bi-level minimum cost consensus model for large-scale group decision-making: A perspective of structural holes
J Qin, D Wang, Y Liang - Information Sciences, 2023 - Elsevier
Large-scale group decision-making (LSGDM) in a social network environment requires the
collaborative consideration of large-scale decision-makers, social network structure, and …
collaborative consideration of large-scale decision-makers, social network structure, and …
Adversarial collaborative neural network for robust recommendation
Most of recent neural network (NN)-based recommendation techniques mainly focus on
improving the overall performance, such as hit ratio for top-N recommendation, where the …
improving the overall performance, such as hit ratio for top-N recommendation, where the …
Federated learning-based cross-enterprise recommendation with graph neural networks
Recommender systems are technology-driven marketing solutions for businesses that
analyze user behavior data. However, collaborative data sharing between enterprises is …
analyze user behavior data. However, collaborative data sharing between enterprises is …