Statecharts: A visual formalism for complex systems
Science of Computer Programming
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A study of mixture models for collaborative filtering
Information Retrieval
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
Learning and adaptivity in interactive recommender systems
Proceedings of the ninth international conference on Electronic commerce
Weighted Boolean conditions for ranking
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
Adaptive provision of evaluation-oriented information: tasks and techniques
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Current recommender systems have to cope with a certain reservation because they are considered to be hard to maintain and to give rather schematic advice. This paper presents an approach to increase maintainability by generating essential parts of the recommender system based on thorough metamodeling. Moreover, preferences are elicited on the basis of user needs rather than product features thus leading to a more user-oriented behavior. The metamodel-based design allows to efficiently adapt all domain-dependent parts of the system.