Overview
FrançaisABSTRACT
This paper presents the basic principles of recommender systems. These systems are strongly developed today yet are invisible to the end user, who perceives only the result, i.e. a list of recommendations. The fields of application of these recommender systems are varied and numerous (suggestions for movies, marketable products, services, etc.). In this paper we present the most representative fields of application. The different dimensions (cultural, legal and algorithmic applications) are also addressed, together with the implementation level through several tools such as Excel, PHP and java / Mahout.
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Read the articleAUTHORS
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Gérald KEMBELLEC: Senior Lecturer - Doctorate in Information and Communication Sciences - CNAM, Information and Communication Devices in the Digital Age Laboratory – Paris, Île-de-France, France
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Max CHEVALIER: Senior Lecturer - Doctorate in Computer Science - Institut de recherche en informatique de Toulouse, University of Toulouse, Toulouse 3, France
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Damien DUDOGNON: R&D Engineer - Doctorate in Computer Science - Overblog, Toulouse, France
INTRODUCTION
In today's digital environment, characterized by an overabundance of information, known as infobesity or the "information deluge", it is clear that human capacities do not allow for exhaustive analysis of the corpus on offer within a platform. Even when using an integrated search engine, relevant results are generally drowned in informational "noise", which prevents them from being found, or at least slows them down. To help the human mind in its selection process, consumer recommendation systems were developed in the last decade of the twentieth century.
A recommendation system is an information filtering tool that helps users make personalized selections from a catalog of items. These systems can be applied in a variety of ways: in social networking sites, in digital marketing with customer relations for online sales, or in personalized services linked to a cultural offering.
After an overview of the fields of application of recommendation engines, the main recommendation strategies are presented from a theoretical and algorithmic point of view. The personalization of these systems can be based on several algorithmic methods, mainly oriented around social aspects and/or the characteristics of the objects handled. This article also highlights the collaborative approach through an example based on open source tools.
With more than 20 years' hindsight on these systems, questions are emerging about ethics, respect for privacy and user trust. As a result, discussions are underway to standardize and provide a legal framework for the phenomenon of recommendation.
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KEYWORDS
Algorithms | recommendation | implementation | social networks | marketing
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Bibliography
Software tools
The R Project for Statistical Computing. Under GPL license, available for Microsoft, Apple, Unix and Linux platforms at URL http://www.r-project.org/ (page consulted August 3, 2015).
BiostatGV property of Institut Pierre Louis d'Épidémiologie et de Santé Publique UMR S 1136, affiliated with INSERM and Université...
Websites
EdgeRank explained on new technology blogTechCrunch by Jason Kincaid http://techcrunch.com/2010/04/22/facebook-edgerank/ (page accessed August 3, 2015).
Events
F8: Facebook's annual conference for developers in 2010 http://cdn.livestream.com/events/f8/ (page consulted on August 3, 2015).
Journées d'études interdisciplinaires sur les systèmes de recommandation pour les chercheurs et professionnels de l'informatique, de la communication et des médias en 2012 et 2015...
Standards and norms
- Online consumer reviews – Principles and requirements for collecting, moderating and reporting online consumer reviews http://www.boutique.afnor.org/norme/nf-z74-501/avis-en-ligne-de-consommateurs-principes-et-exigences-portant-sur-les-processus-de-collecte-moderation-et-restitution-des-avi/article/808897/fa178349 (page consulted on August 3, 2015). - NF Z74-501 AFNOR - 2013
- Online consumer reviews – Principles...
Regulations
Ordinance no. 2011-1012 of August 24, 2011 on the com
munications électroniques http://legifrance.gouv.fr/eli/ordonnance/2011/8/24/2011-1012/jo/texte (page consulted August 3, 2015).
Justice
Yelp complaint against issuers of fake reviews http://www.yelpblog.com/files/2015-02-13-filed-revleap-complaint.pdf (page consulted on August 3, 2015).
Directory
CRM management company
AntVoice, 28 rue du sentier, 75002 Paris
01 83 64 51 08 [email protected]
France http://www.antvoice.com
Literary recommendation company
Babélio, 38 rue de Malte, 75011 Paris
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