The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

Recommender systems are widely used to provide users with recommendations based on
their preferences. With the ever-growing volume of information online, recommender …

In many commercial systems, the'best bet'recommendations are shown, but the predicted
rating values are not. This is usually referred to as a top-N recommendation task, where the …

Recommender systems are the engine of online advertising. Not only do they suggest
movies, music, or romantic partners, but they also are used to select which advertisements to …

Recommender systems have become an important personalization technique on the web
and are widely used especially in e-commerce applications. However, operators of web …

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

Recommender Systems (RSs) help users search large amounts of digital contents and
services by allowing them to identify the items that are likely to be more attractive or useful …

When creating a new software system, or when evolving an existing one, developers do not
reinvent the wheel but, rather, seek available libraries that suit their purpose. In such a …

In this chapter we describe the integration of a recommender system into the production
environment of Fastweb, one of the largest European IP Television (IPTV) providers. The …

In this paper, we address the problem of modeling and predicting the music genre
preferences of users. We introduce a novel user modeling approach, BLL u, which takes into …