most citedAn Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration

14 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2021

Configuring Multiple Instances with Multi-Configuration

Alexander Felfernig, Andrei Popescu, Mathias Uta +5

Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) tha…

cs.AI2021

Anytime Diagnosis for Reconfiguration

Alexander Felfernig, Rouven Walter, Jose A. Galindo +4

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in s…

cs.SE2021

Towards Utility-based Prioritization of Requirements in Open Source Environments

Alexander Felfernig, Martin Stettinger, Müslüm Atas +5

Requirements Engineering in open source projects such as Eclipse faces the challenge of having to prioritize requirements for individual contributors in a more or less unobtrusive…

cs.IR2021

KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting

Martin Stettinger, Trang Tran, Ingo Pribik +5

Existing e-learning environments primarily focus on the aspect of providing intuitive learning contents and to recommend learning units in a personalized fashion. The major focus o…

cs.IR2021★ 14 cited

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

Alexander Felfernig, Viet-Man Le, Andrei Popescu +3

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 equi…