  Recommender systems support decisions in various domains ranging from simple
items such as books and movies to more complex items such as financial
services, telecommunication equipment, and software systems. In this context,
recommendations are determined, for example, on the basis of analyzing the
preferences of similar users. In contrast to simple items which can be
enumerated in an item catalog, complex items have to be represented on the
basis of variability models (e.g., feature models) since a complete enumeration
of all possible configurations is infeasible and would trigger significant
performance issues. In this paper, we give an overview of a potential new line
of research which is related to the application of recommender systems and
machine learning techniques in feature modeling and configuration. In this
context, we give examples of the application of recommender systems and machine
learning and discuss future research issues.
