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History dependent Recommender Systems based on Partial Matching

Armelle Brun 1, * Geoffray Bonnin 1 Anne Boyer 1
* Corresponding author
1 KIWI - Knowledge Information and Web Intelligence
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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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https://hal.inria.fr/inria-00430592
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Submitted on : Monday, November 9, 2009 - 10:50:13 AM
Last modification on : Tuesday, April 24, 2018 - 1:37:16 PM
Long-term archiving on: : Tuesday, October 16, 2012 - 1:30:51 PM

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Armelle Brun, Geoffray Bonnin, Anne Boyer. History dependent Recommender Systems based on Partial Matching. First and Seventeenth International Conference on User Modeling, Adaptation and Personalization - UMAP 2009, Jun 2009, Trento, Italy. pp.343-348, ⟨10.1007/978-3-642-02247-0_34⟩. ⟨inria-00430592⟩

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