There exist situations of decision-making under information overload in the Internet, where
people have an overwhelming number of available options to choose from, eg products to …

Abstract Information extracted from social network services promise to improve the accuracy
of recommender systems in various domains. Against this background, community detection …

Nowadays, the exponential advancement of social networks is creating new application
areas for recommender systems (RSs). People-to-people RSs aim to exploit user's interests …

Recent studies show the benefits of reformulating common machine learning models
through the concept of prototypes–representatives of the underlying data, used to calculate …

Online dating sites have become popular platforms for people to look for potential romantic
partners. Different from traditional user-item recommendations where the goal is to match …

Community detection is of great help to understand the structures and functions of complex
networks. It has become one of popular research topics in the field of complex networks …

With the excessive growth of data over internet, it has become difficult to select relevant
items and information. Recommender systems are the essential tools to handle the …

While social computing related research has focused mostly on how to provide users with
more precise and direct information, or on recommending new search methods to find …

Link prediction is the problem of predicting the location of either unknown or fake links from
uncertain structural information of a network. Link prediction algorithms are useful in gaining …

Collaborative filtering (CF) is out of question the most widely adopted and successful
recommendation approach. A typical CF-based recommender system associates a user with …