19 citations · 62 across the 18 of their papers we have counts for
18 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…
FastDiagP: An Algorithm for Parallelized Direct Diagnosis
Viet-Man Le, Cristian Vidal Silva, Alexander Felfernig +3
Constraint-based applications attempt to identify a solution that meets all defined user requirements. If the requirements are inconsistent with the underlying constraint set, algo…
Conjunctive Query Based Constraint Solving For Feature Model Configuration
Alexander Felfernig, Viet-Man Le, Sebastian Lubos
Feature model configuration can be supported on the basis of various types of reasoning approaches. Examples thereof are SAT solving, constraint solving, and answer set programming…
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…
CoreDiag: Eliminating Redundancy in Constraint Sets
Alexander Felfernig, Christoph Zehentner, Paul Blazek
Constraint-based environments such as configuration systems, recommender systems, and scheduling systems support users in different decision making scenarios. These environments ex…
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…
An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets
Alexander Felfernig, Monika Schubert, Christoph Zehentner
Constraint sets can become inconsistent in different contexts. For example, during a configuration session the set of customer requirements can become inconsistent with the configu…
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…
Consistency-based Merging of Variability Models
Mathias Uta, Alexander Felfernig, Gottfried Schenner +1
Globally operating enterprises selling large and complex products and services often have to deal with situations where variability models are locally developed to take into accoun…
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…
DirectDebug: Automated Testing and Debugging of Feature Models
Viet-Man Le, Alexander Felfernig, Mathias Uta +3
Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logic…
Needs and Challenges for a Platform to Support Large-scale Requirements Engineering. A Multiple Case Study
Davide Fucci, Cristina Palomares, Dolors Costal +12
Background: Requirement engineering is often considered a critical activity in system development projects. The increasing complexity of software, as well as number and heterogenei…