Prof. Alexander Felfernig

TU Graz

Prof. Alexander Felfernig

TU Graz

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Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 December 2020 in Journal of Intelligent Information Systems

Journal Article

Recommender systems in the healthcare domain: state-of-the-art and research issues

Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.

22 June 2017 in Journal of Intelligent Information Systems

Journal Article

An overview of recommender systems in the healthy food domain

Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.

10 June 2011 in Artificial Intelligence for Engineering Design, Analysis and Manufacturing

Journal Article

An efficient diagnosis algorithm for inconsistent constraint sets

Alexander Felfernig

M. Schubert

C. 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 configuration knowledge base. Another example is the engineering phase of a configuration knowledge base where the underlying constraints can become inconsistent with a set of test cases. In such situations we are in the need of techniques that support the identification of minimal sets of faulty constraints that have to be deleted in order to restore consistency. In this paper we introduce a divide and conquer-based diagnosis algorithm (FastDiag) that identifies minimal sets of faulty constraints in an overconstrained problem. This algorithm is specifically applicable in scenarios where the efficient identification of leading (preferred) diagnoses is crucial. We compare the performance of FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis that shows the advantages of our approach.

1 January 2015

Book Chapter

Human Decision Making and Recommender Systems

1 October 2018 in Journal of Intelligent Information Systems

Journal Article

An overview of recommender systems in the internet of things

The Internet Of Things (IoT) is an emerging paradigm that envisions a networked infrastructure enabling different types of devices to be interconnected. It creates different kinds of artifacts (e.g., services and applications) in various application domains such as health monitoring, sports monitoring, animal monitoring, enhanced retail services, and smart homes. Recommendation technologies can help to more easily identify relevant artifacts and thus will become one of the key technologies in future IoT solutions. In this article, we provide an overview of existing applications of recommendation technologies in the IoT context and present new recommendation techniques on the basis of real-world IoT scenarios.

27 May 2017 in Journal of Intelligent Information Systems

Journal Article

An introduction to personalization and mass customization

Juha Tiihonen

Alexander Felfernig

Mass customization as a state-of-the-art production paradigm aims to produce individualized, highly variant products and services with nearly mass production costs. A major side-effect for companies providing complex products and services is that customers quite often get confused by the high variety and do not make a purchase. Personalization technologies can help to alleviate the challenges of mass customization. These technologies support customers in specifying products and services that fit their wishes and needs in a fashion where decision and interaction efforts with sales support systems are significantly reduced. We provide a short overview of related research and the articles that are part of this special issue on Personalization and Mass Customization.

About Me

I am a full professor at the Graz University of Technology (Austria) since March 2009 and received his PhD in Computer Science from the University of Klagenfurt. I direct the Applied Artificial Intelligence (AIG) research group. My research interests include configuration systems, recommender systems, model-based diagnosis, software requirements engineering, different aspects of human decision making, and knowledge acquisition methods. I have published numerous papers in renowned international conferences and journals (e.g., IJCAI, ECAI, ACM Recommender Systems, ACM Intelligent User Interfaces, AI Magazine, Artificial Intelligence, IEEE Transactions on Engineering Management, IEEE Intelligent Systems, Journal of Electronic Commerce) and I ama co-author of the books on “Recommender Systems” published by Cambridge University Press, ”Knowledge-based Configuration” published by Elsevier/Morgan Kaufmann, and “Group Recommender Systems” published by Springer. I also acted as an organizer of international conferences and workshops such as the ACM Conference on Recommender Systems, the International Symposium on Methodologies for Intelligent Systems, and the International Workshop on Configuration. I am a member of the Editorial Board of Applied Intelligence and the Journal of Intelligent Information Systems. I am currently managing TU Graz research in one Horizon2020 project in the area of recommender systems and software requirements engineering.

Prof. Alexander Felfernig

Prof. Alexander Felfernig

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