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Conference papers

Leveraging Rating Behavior to Predict Negative Social Ties

Luc-Aurélien Gauthier 1 Patrick Gallinari 1 Benjamin Piwowarski 2
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
2 BD - Bases de Données
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : User social networks are a useful information for many information access related tasks, such as recommendation or information retrieval. In such tasks, recent papers have exploited the polarity of these links (friend/enemy) by capturing more precisely social patterns. This negative information being relatively scarce, a recent work proposed to infer it in social networks that contain none. However, this work relies on the direct interaction between users. In this paper, we pursue this approach under the assumption that we do not have access to this kind of data neither, thus allowing to cope with most social networks, where users can rate items and have friendship relationships. We exploit the user ratings polarity, i.e the fact that a rating can be positive (like) or negative (dislike), to infer negative ties. Experiments on the Epinions dataset show the potential of our approach.
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Contributor : Benjamin Piwowarski <>
Submitted on : Thursday, September 1, 2016 - 11:25:38 AM
Last modification on : Thursday, March 21, 2019 - 1:21:19 PM



Luc-Aurélien Gauthier, Patrick Gallinari, Benjamin Piwowarski. Leveraging Rating Behavior to Predict Negative Social Ties. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2015, Paris, France. pp.623-628, ⟨10.1145/2808797.2809402⟩. ⟨hal-01358683⟩



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