Learning from User Interactions for Recommending Content in Social Media

  • Mathias Breuss
  • Manos Tsagkias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)

Abstract

We study the problem of recommending hyperlinks to users in social media in the form of status updates. We start with a candidate set of links posted by a user’s social circle (e.g., friends, followers) and rank these links using a combination of (i) a user interaction model, and (ii) the similarity of a user profile and a candidate link. Experiments on two datasets demonstrate that our method is robust and, on average, outperforms, a strong chronological baseline.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mathias Breuss
    • 1
  • Manos Tsagkias
    • 1
  1. 1.ISLAUniversity of AmsterdamAmsterdamThe Netherlands

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