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.
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https://hal.inria.fr/hal-00916241
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Submitted on : Monday, December 9, 2013 - 10:14:07 PM
Last modification on : Saturday, March 23, 2019 - 1:22:48 AM
Long-term archiving on : Thursday, March 20, 2014 - 2:05:19 PM

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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. ⟨hal-00916241⟩

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