A Regularization Method with Inference of Trust and Distrust in Recommender Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10535)

Abstract

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.

Keywords

Recommender systems Collaborative filtering Social relationships Regularization 

Notes

Acknowledgments

Dimitrios Rafailidis was supported by the COMPLEXYS and INFORTECH Research Institutes of University of Mons.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MonsMonsBelgium
  2. 2.Faculty of InformaticsUniversità della Svizzera italianaLuganoSwitzerland

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