@inproceedings{95130deb4f6344369ea89253ad3a5c2f,
title = "Un Mod{\`e}le de Factorisation de Poisson pour la Recommandation de Points d{\textquoteright}Int{\'e}r{\^e}t",
abstract = "The rapid growth of data volumes shared on location-based social networks (LBSN) enables the extraction of users{\textquoteright} preferences. Then those preferences can be used to recommend to the user a list of points-of-interest matching his profile. Today the recommendation of points-of-interest has become an essential component of LBSN. Unfortunately traditional recommendation methods fail to adapt to the specific constraints of LBSN such as the high sparsity of the data, or to take into account the geographical influence. In this paper we present a model of recommendation based on the Poisson factorization that offers an effective solution to these constraints. We have tested our model through experiments on a realistic data set from the LBSN Foursquare. These experiences have enabled us to demonstrate a better recommendation than 3 models of state-of-the art.",
author = "Griesner, \{Jean Beno{\^i}t\} and Talel Abdessalem and Hubert Naacke",
note = "Publisher Copyright: {\textcopyright} 2017 Extraction et Gestion des Connaissances, EGC 2017. All Rights Reserved.; 17e Journees Francophones Extraction et Gestion des Connaissances, EGC 2017 - 17th French-speaking Conference on Knowledge Extraction and Management, EGC 2017? ; Conference date: 24-01-2017 Through 27-01-2017",
year = "2017",
month = jan,
day = "1",
language = "Fran{\c c}ais",
series = "Extraction et Gestion des Connaissances, EGC 2017",
publisher = "Revue des Nouvelles Technologies de l'Information (RNTI)",
pages = "411--416",
editor = "Fabien Gandon and Gilles Bisson",
booktitle = "Extraction et Gestion des Connaissances, EGC 2017",
}