Well-Argued Recommendation: Adaptive Models Based on Words in Recommender Systems

Julien Gaillard 1, 2 Marc El Bèze 1 Eitan Altman 2 Emmanuel Ethis 3
1 LIA - Laboratoire Informatique d'Avignon
2 MAESTRO - Models for the performance analysis and the control of networks
CRISAM - Inria Sophia Antipolis - Méditerranée
3 LCC - Laboratoire Culture et Communication
Abstract : Recommendation systems (RS) take advan- tage of products and users information in order to propose items to consumers. Collaborative, content-based and a few hybrid RS have been developed in the past. In contrast, we propose a new domain-independent semantic RS. By providing textually well-argued recommenda- tions, we aim to give more responsibility to the end user in his decision. The system includes a new similarity measure keeping up both the accuracy of rating predictions and coverage. We propose an innovative way to apply a fast adaptation scheme at a semantic level, provid- ing recommendations and arguments in phase with the very recent past. We have performed several experiments on films data, providing textually well-argued recommendations.
Type de document :
Communication dans un congrès
EMNLP - Conference on Empirical Methods in Natural Language Processing, Oct 2013, Seattle, Washington, United States. pp.1943-1947, 2013
Liste complète des métadonnées

Littérature citée [10 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/hal-00916241
Contributeur : Eitan Altman <>
Soumis le : lundi 9 décembre 2013 - 22:14:07
Dernière modification le : samedi 27 janvier 2018 - 01:31:44
Document(s) archivé(s) le : jeudi 20 mars 2014 - 14:05:19

Fichier

emnlp.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00916241, version 1

Collections

INRIA | TDS-MACS

Citation

Julien Gaillard, Marc El Bèze, Eitan Altman, Emmanuel Ethis. Well-Argued Recommendation: Adaptive Models Based on Words in Recommender Systems. EMNLP - Conference on Empirical Methods in Natural Language Processing, Oct 2013, Seattle, Washington, United States. pp.1943-1947, 2013. 〈hal-00916241〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

399

Téléchargements de fichiers

195

Contact


archive-ouverte@inria.fr