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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationVaMoS'21: 15th International Working Conference on Variability Modelling of Software-Intensive Systems
EditorsPaul Grunbacher
Place of PublicationNew York, NY
PublisherAssociation of Computing Machinery
Pages1-8
Number of pages8
ISBN (Electronic)978-1-4503-8824-5
DOIs
Publication statusPublished - Feb 2021
Event15th International Working Conference on Variability Modelling of Software-Intensive Systems - Virtual from the IMC Krems University of Applied Science, Virtuell, Austria
Duration: 9 Feb 202111 Feb 2021
https://vamos2021.fh-krems.ac.at

Publication series

NameACM International Conference Proceeding Series

Conference

Conference15th International Working Conference on Variability Modelling of Software-Intensive Systems
Abbreviated titleVaMoS 21
CountryAustria
CityVirtuell
Period9/02/2111/02/21
Internet address

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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