Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …

Modern recommender systems appeared during the last decade of the 20th century and
have already proven their importance in tackling with information overload and content or …

Purpose With the exponential growth of the amount of data, the most sophisticated systems
of traditional libraries are not able to fulfill the demands of modern business and user needs …

Collaborative filtering (CF) is one of the most popular recommendation methods, and the co-
rating-based similarity measurement is widely used in CF for predicting ratings of unfamiliar …

Complex networks are widely used in the research of social and biological fields. Analyzing
real community structure in networks is the key to the study of complex networks. Modularity …

In social recommender systems or trust-based recommender systems, malicious users can
bias the recommendations by injecting a large number of fake profiles and by building …

Diversity is a concept relevant to numerous domains of research varying from ecology, to
information theory, and to economics, to cite a few. It is a notion that is steadily gaining …

Collaborative filtering is among the most preferred techniques when implementing
recommender systems. Recently, great interest has turned toward parallel and distributed …

Graph embedding is an important technique used for representing graph structure data that
preserves intrinsic features in a low-dimensional space suitable for graph-based …

Abstract Markov Random Field (MRF) is a powerful framework for developing probabilistic
models of complex problems. MRF models possess rich structures to represent properties …