Learning Recommendations in Social Media Systems by Weighting Multiple Relations

  • Boris Chidlovskii
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)

Abstract

We address the problem of item recommendation in social media sharing systems. We adopt a multi-relational framework capable to integrate different entity types available in the social media system and relations between the entities. We then model different recommendation tasks as weighted random walks in the relational graph. The main contribution of the paper is a novel method for learning the optimal contribution of each relation to a given recommendation task, by minimizing a loss function on the training dataset. We report results of the relation weight learning for two common tasks on the Flickr dataset, tag recommendation for images and contact recommendation for users.

Keywords

Random Walk Loss Function Recommendation System Entity Type Markov Chain Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Boris Chidlovskii
    • 1
  1. 1.Xerox Research Centre EuropeMeylanFrance

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