Recommender Systems

Recommender Systems

Our research in the area of recommender systems comprises all sorts of personalization. We study recommendations in office environments supporting knowlege workers in keeping up to date about resources matching their topics of interest. In recent work we developed several kinds of music recommender systems based on semantic data sources exploiting valuable user-generated content. The link structure of the this data is used to generate explanations for the recommendations provided. Further the team works on filtering algorithms for social network news feeds. All algorithms are deployed either on the Web or on mobile devices respectively.