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Erschienen in:
2006 | OriginalPaper | Buchkapitel
A Preference-Based Recommender System
verfasst von : Benjamin Satzger, Markus Endres, Werner Kießling
Erschienen in: E-Commerce and Web Technologies
Verlag: Springer Berlin Heidelberg
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The installation of recommender systems in e-applications like online shops is common practice to offer alternative or cross-selling products to their customers. Usually collaborative filtering methods, like e.g. the Pearson correlation coefficient algorithm, are used to detect customers with a similar taste concerning some items. These customers serve as recommenders for other users. In this paper we introduce a novel approach for a recommender system that is based on user preferences, which may be mined from log data in a database system. Our notion of user preferences adopts a very powerful preference model from database systems. An evaluation of our prototype system suggests that our prediction quality can compete with the widely-used Pearson-based approach. In addition, our approach can achieve an added value, because it yields better results when there are only a few recommenders available. As a unique feature, preference-based recommender systems can deal with multi-attribute recommendations.