Research on recommender systems typically focuses on the accuracy of prediction
algorithms. Because accuracy only partially constitutes the user experience of a …

Recently, food recommender systems have received increasing attention due to their
relevance for healthy living. Most existing studies on the food domain focus on …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

In this age of information overload, people use a variety of strategies to make choices about
what to buy, how to spend their leisure time, and even whom to date. Recommender …

In recent years, there has been an increased interest in more user-centered evaluation
metrics for recommender systems such as those mentioned in [49]. It has also been …

Knowledge-based Configuration incorporates knowledge representation formalisms to
capture complex product models and reasoning methods to provide intelligent interactive …

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 …

Users of social recommender systems may want to inspect and control how their social
relationships influence the recommendations they receive, especially since …

Both content-based and collaborative systems require a significant amount of data about
past buying and rating experiences. For example, collaborative systems require a …

Recommender systems have already proved to be valuable for co** with the information
overload problem in several application domains. They provide people with suggestions for …