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

TL;DR

This paper reviews how recommender systems and machine learning techniques are applied to feature modeling and configuration, especially for complex items where enumeration is infeasible, highlighting future research directions.

Contribution

It provides an overview of integrating recommender systems and machine learning into feature modeling and configuration, proposing new research avenues.

Findings

Recommender systems aid in complex feature configuration.

Machine learning enhances feature model analysis.

Future research directions are discussed.

Abstract

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.