19 citations · 44 across the 13 of their papers we have counts for
16 papers
Solving Multi-Configuration Problems: A Performance Analysis with Choco Solver
Benjamin Ritz, Alexander Felfernig, Viet-Man Le +1
In many scenarios, configurators support the configuration of a solution that satisfies the preferences of a single user. The concept of \emph{multi-configuration} is based on the…
Concentrating on the Impact: Consequence-based Explanations in Recommender Systems
Sebastian Lubos, Thi Ngoc Trang Tran, Seda Polat Erdeniz +4
Recommender systems assist users in decision-making, where the presentation of recommended items and their explanations are critical factors for enhancing the overall user experien…
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…
AI Techniques for Software Requirements Prioritization
Alexander Felfernig
Aspects such as limited resources, frequently changing market demands, and different technical restrictions regarding the implementation of software requirements (features) often d…
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…