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 language | English |
---|---|
Title of host publication | VaMoS'21: 15th International Working Conference on Variability Modelling of Software-Intensive Systems |
Editors | Paul Grunbacher |
Place of Publication | New York, NY |
Publisher | Association of Computing Machinery |
Pages | 1-8 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-4503-8824-5 |
DOIs | |
Publication status | Published - Feb 2021 |
Event | 15th International Working Conference on Variability Modelling of Software-Intensive Systems - Virtual from the IMC Krems University of Applied Science, Virtuell, Austria Duration: 9 Feb 2021 → 11 Feb 2021 https://vamos2021.fh-krems.ac.at |
Publication series
Name | ACM International Conference Proceeding Series |
---|
Conference
Conference | 15th International Working Conference on Variability Modelling of Software-Intensive Systems |
---|---|
Abbreviated title | VaMoS 21 |
Country/Territory | Austria |
City | Virtuell |
Period | 9/02/21 → 11/02/21 |
Internet address |
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications