Traditionally, the field of recommender systems has evaluated the fruits of its labor using
metrics of algorithmic accuracy and precision (see Chap. 8 for an overview of recommender …

Personalized recommendation brings about novel challenges in ensuring fairness,
especially in scenarios in which users are not the only stakeholders involved in the …

Recommender systems suggest items by exploiting the interactions of the users with the
system (eg, the choice of the movies to recommend to a user is based on those she …

Recommender Systems have to deal with a wide variety of users and user types that
express their preferences in different ways. This difference in user behavior can have a …

Recommender systems have been upgraded, tested, and applied in many, often
incomparable ways. In attempts to diligently understand user behavior in certain …

Recommender Systems need to deal with different types of users who represent their
preferences in various ways. This difference in user behaviour has a deep impact on the …

Recommender systems usually suggest items by exploiting all the previous interactions of
the users with a system (eg, in order to decide the movies to recommend to a user, all the …

This chapter provides a self-contained overview on the basics of collaborative filtering
recommender systems. It covers two main classes of recommendation scenarios. In the …

Evaluating the Accuracy and Utility of Recommender Systems Page 1 Evaluating the Accuracy
and Utility of Recommender Systems vorgelegt von Master of Science Alan Said Von der …

The rating data collected by the recommender system usually contains noise due to external
factors such as human uncertainty and inconsistency. Such noise, usually modeled by a …