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.
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