Abstract in Englisch:

The field of recommender systems has seen substantial progress in recent years in terms of algorithmic sophistication and quality of recommendations as measured by standard accuracy metrics. Yet, the systems mainly act as black boxes for the user and are limited in their capability to explain why certain items are recommended. This is particularly true when using abstract models which do not easily lend themselves for providing explanations. In many cases, however, recommendation methods are employed in scenarios where users not only rate items, but also provide feedback in the form of tags or written product reviews. Such user-generated content can serve as a useful source for deriving explanatory information that may increase the user’s understanding of the underlying criteria and mechanisms that led to the results. In this paper, we describe a set of developments we undertook to couple such textual content with common recommender techniques. These developments have moved from integrating tags into collaborative filtering to employing topics and sentiments expressed in reviews to increase transparency and to give users more control over the recommendation process. Furthermore, we describe our current research goals and a first concept concerning extraction of more complex argumentative explanations from textual reviews and presenting them to users.