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Facing Uncertainty in Link Recommender Systems

Jean-Yves Delort 1 Bernadette Bouchon-Meunier 1 
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Most of prefetching and web recommender systems require a learning phase on a users behavior database. In most of the situations data are the outcome of a preprocessing task of HTTP log files which contain information intrinsically uncertain. This paper deals with modelization of this uncertainty in a link recommendation perspective. A new algorithm based on evidence theory is presented. In addition, new general characterizations of recommender systems are introduced.
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Submitted on : Wednesday, July 12, 2017 - 5:09:16 PM
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  • HAL Id : hal-01561422, version 1


Jean-Yves Delort, Bernadette Bouchon-Meunier. Facing Uncertainty in Link Recommender Systems. 11th International World Wide Web Conference, May 2002, Honolulu, Hawaii, United States. ⟨hal-01561422⟩



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