Users are often confronted with situations where they have to decide in favor or against an
offered item, like a book, movie, or recipe. Those suggested items are commonly determined …

This open access book provides a basic introduction to feature modelling and analysis as
well as to the integration of AI methods with feature modelling. It is intended as an …

As Artificial Intelligence (AI) becomes ubiquitous, the need for Explainable AI (XAI) has
become critical for transparency and trust among users. A significant challenge in XAI is …

In recommender systems, the presentation of explanations plays a crucial role in supporting
users' decision-making processes. Although numerous existing studies have focused on the …

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

Generating free-text rationales is among the emergent capabilities of Large Language
Models (LLMs). These rationales have been found to enhance LLM performance across …

In the evolving landscape of human-centered AI, fostering a synergistic relationship between
humans and AI agents in decision-making processes stands as a paramount challenge. This …

One of the new trends in the development of recommendation algorithms is the
dissemination of their capabilities to support the population in managing their health, in …

In recommender systems, the presentation of explanations plays a crucial role in supporting
users' decision-making processes. Although numerous existing studies have focused on the …

Abstract Feature Models (FMs) are not only an active scientific topic but they are supported
by many tools from industry and academia. In this chapter, we provide an overview of …