19 citations · 21 across the 6 of their papers we have counts for
7 papers
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…
Designing Explanations for Group Recommender Systems
A. Felfernig, N. Tintarev, T. N. T. Trang +1
Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want…
An Overview of Direct Diagnosis and Repair Techniques in the WeeVis Recommendation Environment
Alexander Felfernig, Stefan Reiterer, Martin Stettinger +1
Constraint-based recommenders support users in the identification of items (products) fitting their wishes and needs. Example domains are financial services and electronic equipmen…
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…
Recommender Systems for Configuration Knowledge Engineering
Alexander Felfernig, Stefan Reiterer, Martin Stettinger +3
The knowledge engineering bottleneck is still a major challenge in configurator projects. In this paper we show how recommender systems can support knowledge base development and m…
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…