Learning from user interactions for recommending content in social media

Authors
Publication date 2014
Host editors
  • M. de Rijke
  • T. Kenter
  • A.P. de Vries
  • C.X. Zhai
  • F. de Jong
  • K. Radinsky
  • K. Hofmann
Book title Advances in Information Retrieval
Book subtitle 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014: proceedings
ISBN
  • 9783319060279
ISBN (electronic)
  • 9783319060286
Series Lecture Notes in Computer Science
Event 36th European Conference on Information Retrieval (ECIR'14)
Pages (from-to) 598-604
Publisher Cham: Springer
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract We study the problem of recommending hyperlinks to users in social media. 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.
Document type Conference contribution
Language English
Published at https://doi.org/10.1007/978-3-319-06028-6_63
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