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Representation of Propositional Data for Collaborative Filtering

  • Andrzej SzwabeEmail author
  • Pawel Misiorek
  • Michal Ciesielczyk
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)

Abstract

State-of-the-art approaches to collaborative filtering are based on the use of an input matrix that represents each user profile as a vector in a space of items and, analogically, each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples, one has to propose a bi-relational data representation that is more flexible than the ordinary user-item ratings matrix. We propose to use a matrix, in which columns represent RDF-like triples and rows represent users, items, and relations. We show that the proposed behavioral data representation based on the use of an element-fact matrix, combined with reflective matrix processing, enables outperforming state-of-the- art collaborative filtering methods based on the use of a ’standard’ user-item matrix.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Andrzej Szwabe
    • 1
    Email author
  • Pawel Misiorek
    • 1
  • Michal Ciesielczyk
    • 1
  1. 1.Institute of Control and Information EngineeringPoznan University of TechnologyPoznanPoland

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