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Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

  • Vincent Schickel-Zuber
  • Boi Faltings
Conference paper
  • 492 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4198)

Abstract

To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction models. We propose a new method that extends the utility model and assumes that the structure of user preferences follows an ontology of product attributes. Using the data of the MovieLens system, we show experimentally that real user preferences indeed closely follow an ontology based on movie attributes. Furthermore, a recommender based just on a single individual’s preferences and this ontology performs better than collaborative filtering, with the greatest differences when little data about the user is available. This points the way to how proper inductive bias can be used for significantly more powerful recommender systems in the future.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vincent Schickel-Zuber
    • 1
  • Boi Faltings
    • 1
  1. 1.School of Computer and Communication Sciences – ICSwiss Federal Institute of Technology (EPFL)LausanneSwitzerland

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