An Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration
This is an incremental overview paper discussing potential research directions for improving configuration decisions in domains with complex items, such as telecommunications and software.
The paper provides an overview of applying recommender systems and machine learning to feature modeling and configuration for complex items like software systems, where traditional item catalogs are infeasible, but it does not present specific results or numbers.
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.