Flash reactivity: adaptive models in recommender systems

Abstract : Recommendation systems take advantage of products and users information in order to propose items to targeted consumers. Collaborative recommendation systems, content-based recommendation systems and a few hybrid systems have been developed. We propose a dynamic and adaptive framework to overcome the usual issues of nowadays systems. We present a method based on adaptation in time in order to provide recommendations in phase with the present instant. The system includes a dynamic adaptation to enhance the accuracy of rating predictions by applying a new similarity measure. We did several experiments on films data from Vodkaster, showing that systems incorporating dynamic adaptation improve significantly the quality of recommendations compared to static ones.
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DMIN - 9th International Conference on Data Mining, Jul 2013, Las Vegas, Nevada, United States. 2013
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Dernière modification le : samedi 27 janvier 2018 - 01:31:44
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  • HAL Id : hal-00913189, version 1

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Julien Gaillard, Marc El Bèze, Eitan Altman, Emmanuel Ethis. Flash reactivity: adaptive models in recommender systems. DMIN - 9th International Conference on Data Mining, Jul 2013, Las Vegas, Nevada, United States. 2013. 〈hal-00913189〉

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