Probabilistic Approaches to Recommendations

Nicola Barbieri is a post-doc in the WebMining research group at Yahoo! Labs - Barcelona. He graduated with full marks and honor and received his Ph.D. in 2012 at University of Calabria, Italy. Before joining Yahoo in 2012, he was a fellow researcher at ICAR-CNR. His research focuses on the development of novel data mining and machine learning techniques with a wide range of applications in social influence analysis, viral marketing, and community detection. Giuseppe Manco received a Ph.D. degree in computer science from the University of Pisa. He is currently a senior researcher at the Institute of High Performance Computing and Networks (ICAR-CNR) of the National Research Council of Italy and a contract professor at University of Calabria, Italy. He has been contract researcher at the CNUCE Institute in Pisa, Italy. His current research interests include knowledge discovery and data mining, Recommender systems, and Social Network analysis. Ettore Ritacco is a researcher at the Institute of High Performance Computing and Networks (ICAR-CNR) of the National Research Council of Italy. He graduated summa cum laude in Computer Science and received his Ph.D. in the doctoral school in System Engineering and Computer Science (cycle XXIII), 2011, at University of Calabria, Italy. His research focuses on mathematical tools for knowledge discovery, business intelligence and data mining. His current interests are Recommender Systems, Social Network analysis, and mining complex data in hostile environments.

• Preface.- The Recommendation Process.- Probabilistic Models for Collaborative Filtering.- Bayesian Modeling.- Exploiting Probabilistic Models.- Contextual Information.- Social Recommender Systems.- Conclusions.- Bibliography.- Authors' Biographies .