Using Skipping for Sequence-Based Collaborative Filtering

Geoffray Bonnin 1 Armelle Brun 1 Anne Boyer 1
1 KIWI - Knowledge Information and Web Intelligence
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Recommender systems filter resources for a given user by predicting the most pertinent resource given a specific context. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream. The underlying hypothesis is that the resources order in the stream results from the intrinsic logic of the user's behavior. The Sequence Based Recommender we propose is inspired from Language Modeling and integrates skipping techniques. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous sequences to compute recommendations enhances the accuracy. Moreover, we propose a skipping variant that provides a high accuracy while being less complex.
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Communication dans un congrès
IEEE/WIC/ACM International Conference on Web Intelligence, Dec 2008, Sydney, Australia. 2008
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https://hal.inria.fr/inria-00332238
Contributeur : Geoffray Bonnin <>
Soumis le : lundi 20 octobre 2008 - 14:57:35
Dernière modification le : mardi 24 avril 2018 - 13:37:19

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  • HAL Id : inria-00332238, version 1

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Geoffray Bonnin, Armelle Brun, Anne Boyer. Using Skipping for Sequence-Based Collaborative Filtering. IEEE/WIC/ACM International Conference on Web Intelligence, Dec 2008, Sydney, Australia. 2008. 〈inria-00332238〉

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