A Preference-Based Recommender System View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006

AUTHORS

Benjamin Satzger , Markus Endres , Werner Kießling

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. More... »

PAGES

31-40

References to SciGraph publications

Book

TITLE

E-Commerce and Web Technologies

ISBN

978-3-540-37743-6
978-3-540-37745-0

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11823865_4

DOI

http://dx.doi.org/10.1007/11823865_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037879520


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