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Article | REF: H7245 V1

Recommender systems

Authors: Gérald KEMBELLEC, Max CHEVALIER, Damien DUDOGNON

Publication date: November 10, 2015

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ABSTRACT

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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AUTHORS

  • 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

  • Max CHEVALIER: Senior Lecturer - Doctorate in Computer Science - Institut de recherche en informatique de Toulouse, University of Toulouse, Toulouse 3, France

  • 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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