Social recommendations at work

  • Authors:

  • Tom Crecelius;Mouna Kacimi;Sebastian Michel;Thomas Neumann;Josiane X. Parreira;Ralf Schenkel;Gerhard Weikum

  • Affiliations:

  • Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany;École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany

  • Venue:
  • Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2008

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Abstract

Online communities have become popular for publishing and searching content, and also for connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. Items can be annotated and rated by different users, and users can connect to others that are usually friends and/or share common interests. We demonstrate a social recommendation system that takes advantages of users connections and tagging behavior to compute recommendations of items in such communities. The advantages can be verified via comparison to a standard IR technique.