The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available. Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy. We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations.

Autorentext

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.