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A Low-Order Markov Model integrating Long-Distance Histories for Collaborative Recommender Systems

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 provide users with pertinent resources according their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long and short-distance resources in the history with a tractable model. The Skipping Based Recommender we propose uses Markov models inspired from the ones used in language modeling while integrating skipping techniques to handle noise during navigation. Weighting schemes are also used to alleviate the importance of distant resources. This recommender has also the characteristic to be anytime. 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 resources to compute recommendations enhances the accuracy. Moreover, the skipping variant we propose provides a high accuracy while being less complex than state of the art variants.
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Contributor : Geoffray Bonnin Connect in order to contact the contributor
Submitted on : Tuesday, November 25, 2008 - 2:13:25 PM
Last modification on : Friday, February 26, 2021 - 3:28:08 PM


  • HAL Id : inria-00341537, version 1



Geoffray Bonnin, Armelle Brun, Anne Boyer. A Low-Order Markov Model integrating Long-Distance Histories for Collaborative Recommender Systems. International Conference on Intelligent User Interfaces (IUI), Feb 2009, Sanibel Island, United States. ⟨inria-00341537⟩



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