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.
