activity
20212026
most citedRecommender Systems for Sustainability: Overview and Research Issues

58 citations · 150 across the 15 of their papers we have counts for

collaborators

15 papers

cs.SE2026★ 1 cited

Usability Analysis of Configurator User Interfaces with Multimodal Large Language Models

Sebastian Lubos, Alexander Felfernig, Damian Garber +11

Configuration is a key technology for tailoring complex software systems, services, and products. A successful application of configurators not only depends on technical correctnes…

cs.SE2026

Early-Stage Product Line Validation Using LLMs: A Study on Semi-Formal Blueprint Analysis

Viet-Man Le, Thi Ngoc Trang Tran, Sebastian Lubos +2

We study whether Large Language Models (LLMs) can perform feature model analysis operations (AOs) directly on semi-formal textual blueprints, i.e., concise constrained-language des…

cs.HC2025

Towards LLM-Based Usability Analysis for Recommender User Interfaces

Sebastian Lubos, Alexander Felfernig, Damian Garber +2

Usability is a key factor in the effectiveness of recommender systems. However, the analysis of user interfaces is a time-consuming process that requires expertise. Recent advances…

cs.IR2025

Towards LLM-Enhanced Product Line Scoping

Alexander Felfernig, Damian Garber, Viet-Man Le +2

The idea of product line scoping is to identify the set of features and configurations that a product line should include, i.e., offer for configuration purposes. In this context,…

cs.IR2025

Sustainability Evaluation Metrics for Recommender Systems

Alexander Felfernig, Damian Garber, Viet-Man Le +2

Sustainability-oriented evaluation metrics can help to assess the quality of recommender systems beyond wide-spread metrics such as accuracy, precision, recall, and satisfaction. F…

cs.IR2025

Towards LLM-Enhanced Group Recommender Systems

Sebastian Lubos, Alexander Felfernig, Thi Ngoc Trang Tran +5

In contrast to single-user recommender systems, group recommender systems are designed to generate and explain recommendations for groups. This group-oriented setting introduces ad…

cs.IR2024★ 58 cited

Recommender Systems for Sustainability: Overview and Research Issues

Alexander Felfernig, Manfred Wundara, Thi Ngoc Trang Tran +5

Sustainability development goals (SDGs) are regarded as a universal call to action with the overall objectives of planet protection, ending of poverty, and ensuring peace and prosp…

cs.SE2024★ 54 cited

Leveraging LLMs for the Quality Assurance of Software Requirements

Sebastian Lubos, Alexander Felfernig, Thi Ngoc Trang Tran +4

Successful software projects depend on the quality of software requirements. Creating high-quality requirements is a crucial step toward successful software development. Effective…

cs.IR2023★ 1 cited

Sports Recommender Systems: Overview and Research Issues

Alexander Felfernig, Manfred Wundara, Thi Ngoc Trang Tran +3

Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sport.…

cs.IR2023★ 2 cited

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…

cs.AI2023★ 13 cited

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…

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.IR2021★ 2 cited

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…

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…

cs.SE2021★ 5 cited

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…