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Inférence de liens signés dans les réseaux sociaux, par apprentissage à partir d'interactions utilisateur

Luc-Aurélien Gauthier 1
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In this thesis, we study the semantic of relations between users and, in particular, the antagonistic forces we naturally observe in various social relationships, such as hostility or suspicion. The study of these relationships raises many problems both techniques - because the mathematical arsenal is not really adapted to the negative ties - and practical, due to the difficulty of collecting such data (explaining a negative relationship is perceived as intrusive and inappropriate for many users). That’s why we focus on the alternative solutions consisting in inferring these negative relationships from more widespread content. We use the common judgments about items the users share, which are the data used in recommender systems. We provide three contributions, described in three distinct chapters. In the first one, we discuss the case of agreements about items that may not have the same semantics if they involve appreciated items or not by two users. We will see that disliking the same product does not mean similarity. Afterward, we consider in our second contribution the distributions of user ratings and items ratings in order to measure whether the agreements or disagreements may happen by chance or not, in particular to avoid the user and item biases observed in this type of data. Our third contribution consists in using these results to predict the sign of the links between users from the only positive ties and the common judgments about items, and then without any negative social information.
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Luc-Aurélien Gauthier. Inférence de liens signés dans les réseaux sociaux, par apprentissage à partir d'interactions utilisateur. Réseaux sociaux et d'information [cs.SI]. Université Pierre et Marie Curie - Paris VI, 2015. Français. ⟨NNT : 2015PA066639⟩. ⟨tel-01344612⟩

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