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History Dependent Recommender Systems Based on Partial Matching

  • Armelle Brun
  • Geoffray Bonnin
  • Anne Boyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)

Abstract

This paper focuses on the utilization of the history of navigation within recommender systems. It aims at designing a collaborative recommender based on Markov models relying on partial matching in order to ensure high accuracy, coverage, robustness, low complexity while being anytime.

Indeed, contrary to state of the art, this model does not simply match the context of the active user to the context of other users but partial matching is performed: the history of navigation is divided into several sub-histories on which matching is performed, allowing the matching constraints to be weakened. The resulting model leads to an improvement in terms of accuracy compared to state of the art models.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Armelle Brun
    • 1
  • Geoffray Bonnin
    • 1
  • Anne Boyer
    • 1
  1. 1.LORIA LaboratoryUniversity Nancy 2Vandœuvre les Nancy

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