Challenges in Tag Recommendations for Collaborative Tagging Systems

  • Robert JäschkeEmail author
  • Andreas Hotho
  • Folke Mitzlaff
  • Gerd Stumme
Part of the Intelligent Systems Reference Library book series (ISRL, volume 32)


Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags. Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system.

The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time.

In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.


Recommender System Collaborative Filter Social Bookmark Recommendation Task Online Task 
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 2012

Authors and Affiliations

  • Robert Jäschke
    • 1
    Email author
  • Andreas Hotho
    • 2
  • Folke Mitzlaff
    • 1
  • Gerd Stumme
    • 1
  1. 1.Knowledge & Data Engineering GroupUniversity of KasselKasselGermany
  2. 2.Data Mining and Information Retrieval Group at LS VIUniversity of WürzburgWürzburgGermany

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