User preferences are a crucial input needed by recommender systems to determine relevant
items. In single-shot recommendation scenarios such as content-based filtering and …

Recommender systems support decisions in various domains ranging from simple items
such as books and movies to more complex items such as financial services …

Mass customization as a state-of-the-art production paradigm aims to produce
individualized, highly variant products and services with nearly mass production costs. A …

In the previous chapters, we have learned how to design group recommender systems but
did not explicitly discuss how to evaluate them. The evaluation techniques for group …

In this chapter, our aim is to show how group recommendation can be implemented on the
basis of recommendation paradigms for individual users. Specifically, we focus on …

Feature models are used to represent variability properties of complex items. In most of the
cases, the assumption in feature model configuration is that single users/stakeholders are …

Systems involving artificial intelligence (AI) are protagonists in many everyday activities.
Moreover, designers are increasingly implementing these systems for groups of users in …

Explanations are used in recommender systems for various reasons. Users have to be
supported in making (high-quality) decisions more quickly. Developers of recommender …

The advent of extended reality (XR) technologies is opening new doors for augmenting
customer experience and enhancing sales processes. XR is promising not only for …

Recommender systems are decision support systems helping users to identify one or more
items (solutions) that fit their wishes and needs. The most frequent application of …