Mining User Behavior in Social Environments

The growth of the Web 2.0 has brought to a widespread use of social media systems and to an increasing number of active users. This phenomenon implies that each user interacts with too many users and is overwhelmed by a huge amount of content, leading to the well know social interaction overload problem. In order to address this problem several research communities study Social Recommender Systems, which are information filtering systems that operate in the social media domain and aim at suggesting to the users items that are supposed to be interesting for them. This book proposes some social recommendation approaches based on the mining of the user behavior, i.e., on the exploitation of the activity of the users in social environments, in order to produce accurate and up-to-date recommendations.

Autorentext

Matteo Manca earned his Ph.D. in CS from the University of Cagliari in May 2014. His interests include Social Recommender Systems and Machine Learning. Ludovico Boratto is a postdoc of the University of Cagliari. His interests are on recommender systems, web mining algorithms.Salvatore Carta is associate professor of the University of Cagliari.