Abstract
How can you discover something new, that matches your interest? Recommender Systems have been studied since the 90ies. Their benefit comes from guiding a user through the density of the information jungle to useful knowledge clearings. Early research on recommender systems focuses on al- gorithms and their evaluation to improve recommendation accuracy using F-measures and other methodologies from signal-detection theory. Present research includes other aspects such as human factors that affect the user experience and interactive visualization techniques to support transparency of results and user control. In this paper, we analyze all publications on recommender systems from the scopus database, and particularly also papers with such an HCI focus. Based on an analysis of these papers, future topics for recommender systems research are identified, which include more advanced support for user control, adaptive interfaces, affective computing and applications in high risk domains.