DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2017-09-18 - Colloque/Article dans les actes avec comité de lecture - Anglais - 16 page(s)

Rafailidis Dimitrios , Crestani Fabio, "A Regularization Method with Inference of Trust and Distrust in Recommender Systems" in European Conference on Machine Learning, Skopje, FYROM, 2017

  • Codes CREF : Informatique générale (DI1162)
  • Unités de recherche UMONS : Systèmes d'information (S832)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut de Recherche sur les Systèmes Complexes (Complexys)
  • Centres UMONS : Modélisation mathématique et informatique (CREMMI)

Abstract(s) :

(Anglais) In this study we investigate the recommendation problem with trust and distrust relationships to overcome the sparsity of users' preferences, accounting for the fact that users trust the recommendations of their friends, and they do not accept the recommendations of their foes. In addition, not only users' preferences are sparse, but also users' social relationships. So, we first propose an inference step with multiple random walks to predict the implicit-missing trust relationships that users might have in recommender systems, while considering users' explicit trust and distrust relationships during the inference. We introduce a regularization method and design an objective function with a social regularization term to weigh the influence of friends' trust and foes' distrust degrees on users' preferences. We formulate the objective function of our regularization method as a minimization problem with respect to the users' and items' latent features and then we solve our recommendation problem via gradient descent. Our experiments confirm that our approach preserves relatively high recommendation accuracy in the presence of sparsity in both the users' preferences and social relationships, significantly outperforming several state-of-the-art methods.