  Recommender systems have become a pervasive part of our daily online
experience, and are one of the most widely used applications of artificial
intelligence and machine learning. Therefore, regulations and requirements for
trustworthy artificial intelligence, for example, the European AI Act, which
includes notions such as transparency, privacy, and fairness are also highly
relevant for the design of recommender systems in practice. This habilitation
elaborates on aspects related to these three notions in the light of
recommender systems, namely: (i) transparency and cognitive models, (ii)
privacy and limited preference information, and (iii) fairness and popularity
bias in recommender systems. Specifically, with respect to aspect (i), we
highlight the usefulness of incorporating psychological theories for a
transparent design process of recommender systems. We term this type of systems
psychology-informed recommender systems. In aspect (ii), we study and address
the trade-off between accuracy and privacy in differentially-private
recommendations. We design a novel recommendation approach for collaborative
filtering based on an efficient neighborhood reuse concept, which reduces the
number of users that need to be protected with differential privacy.
Furthermore, we address the related issue of limited availability of user
preference information, e.g., click data, in the settings of session-based and
cold-start recommendations. With respect to aspect (iii), we analyze popularity
bias in recommender systems. We find that the recommendation frequency of an
item is positively correlated with this item's popularity. This also leads to
the unfair treatment of users with little interest in popular content. Finally,
we study long-term fairness dynamics in algorithmic decision support in the
labor market using agent-based modeling techniques.
