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- W1531217405 abstract "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." @default.
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- W1531217405 date "2006-01-01" @default.
- W1531217405 modified "2023-09-25" @default.
- W1531217405 title "A Preference-Based Recommender System" @default.
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- W1531217405 doi "https://doi.org/10.1007/11823865_4" @default.
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