Cloud-based visualization services have made visual analytics accessible to a much wider
audience than ever before. Systems such as Tableau have started to amass increasingly …

In this paper, we shed light on explaining user models for transparent recommendation
while considering user personal characteristics. To this end, we developed a transparent …

Online question pools like LeetCode provide hands-on exercises of skills and knowledge.
However, due to the large volume of questions and the intent of hiding the tested knowledge …

Adding explanations to recommender systems is said to have multiple benefits, such as
increasing user trust or system transparency. Previous work from other application areas …

Recommender systems aim at supporting users in their search and decision making process
by selecting a small number of likely relevant items from a large set of options. Although …

Enhancing explainability in recommender systems has drawn more and more attention in
recent years. In this paper, we address two aspects that are under-investigated in …

This contribution sheds light on the potential of transparent user models for self-
actualization. It discusses the development of EDUSS, a conceptual framework for self …

Data containing geospatial semantics, such as geotagged tweets, travel blogs, and crime
reports, associates natural language texts with geographical locations. This paper presents …

Supporting personal health with Decision Support Systems (DSS) and, specifically,
recommender systems (RS) is a promising and growing area of research. Integrating the …

Recommender systems can help web users find more relevant content, improve their online
experience, and support them in the discovery of new Points-of-Interest (POI). Yet …