Gross, TomZiegler, Jürgen2017-11-202017-11-202015https://dl.gi.de/handle/20.500.12116/6174Group recommender systems make suggestions to groups of users who want to share experiences or products. Despite their high potential for helping users, GRS face diverse challenges that can be clustered into two groups: predictions and processes. Generating predictions of the goodness of the fit of recommendations to the group has been seen as a core challenge of recommender systems from their beginning, while supporting the processes of discussion for reaching consensus on the item to pick is a more recent challenge. In this paper I report on a base platform for GRS with powerful algorithms for generating and explaining recommendations with high predictions, and an easy and effective process model for GRS.Group Recommender SystemsPredictionAlgorithmText/Conference Paper2196-6826