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

An Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration