The federated recommendation system (FedRS), which is the application of the
recommendation system (RS) in federated learning, has been creatively developed as …

The ever-increasing number of users and items continuously imposes new challenges to
existent clustering-based recommendation algorithms. To better simulate the interactions …

The most significant advantage of the group recommender system over personalization is
the low computational cost because the former analyzes the preferences of many users at …

In recent years, graph neural networks have played a very important role in graph data
analysis, and the application of graph convolutional networks (GCN) to recommender …

Organizing user-item interaction data into a graph has brought many benefits to
recommendation methods. Compared with the user-item bipartite graph structure, a …

Recommender systems (RS) are substantial for online shopping or digital content services.
However, due to some data characteristics or insufficient historical data, may encounter …

In recommendation systems, users often click on some items that are distinct from historically
clicked items. This verifies the existence of interest gaps between the historical interests …

Recommendation system is widely used because of its personalized service. It helps users
obtain satisfactory results due to ambiguous expression in search engines. However, with …

In information theory—as well as in the adjacent fields of statistics, machine learning,
artificial intelligence, signal processing and pattern recognition—many flexibilizations of the …

The grey-wolf optimizer (GWO) is a comparatively recent and competent algorithm in Swarm
Intelligence (SI) to solve numerical and real-world optimization problems. However, the …