Alexander Felfernig is 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. He directs the Applied Software Engineering (ASE) research group. His research interests include configuration systems, recommender systems, model-based diagnosis, software requirements engineering, different aspects of human decision making, and knowledge acquisition methods. Alexander Felfernig has published numerous papers in renowned international conferences and journals (e.g., IJCAI, ECAI, ACM Recommender Systems, ACM Intelligence User Interfaces, AI Magazine, Artificial Intelligence, IEEE Transactions on Engineering Management, IEEE Intelligent Systems, Journal of Electronic Commerce) and is a co-author of the books on “Recommender Systems” published by Cambridge University Press and ”Knowledge-based Configuration – From Research to Business Cases” published by Elsevier/Morgan Kaufmann. He 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 Configuraiton. Currently, he is a member of the Editorial Board of Applied Intelligence and the Journal of Intelligent Information Systems.
Recommendation Technologies for Configurable Products
- Approaches to Group-based Configuration: An assumption of existing configuration environments is that there is no need for additional configuration support in scenarios where groups of users are jointly configuring their preferred product or service. A major consequence of this assumption is that single users are forced to encode the preferences of a group which is often done in a suboptimal fashion. We will show how technologies from group decision making and recommender systems can be exploited to support groups of users when configuring products and services. In this context we will focus on different types of group decision heurstics and show how these heuristics can be applied to recommend configurations to groups.
- Reducing the Knowledge Acquisition Bottleneck: The formalization of product domain knowledge is still a costly process. It is important to understand in which way formalized knowledge is easy to understand for engineers but also in which way configuration knowledge should be represented on an informal level such that it can easily be transferred into a corresponding formal representation. We present results of empirical studies that show the impact of the chosen knowledge representation on the ease of corresponding knowledge base development and maintenance processes. In this context we also show how advanced testing and debugging methods can help to improve the efficiency of knowledge base development and maintenance.