@inproceedings{marinho:ecml2009,
  abstract = {Textual contents associated to resources are considered as sources of candidate tags to improve the performance of tag recommenders in social tagging systems. In this paper, we propose a twolevel learning hierarchy of a concept based keyword extraction method to filter the candidate tags and rank them based on their occurrences in concepts existing in the given resources. Incorporating user-created tags to extract the hidden concept-document relationships distinguishes the two-level from the one-level learning version, which extracts concepts directly using terms existing in textual contents. Our experiment shows that a multi-concept approach, which considers more than one concept for each resource, improves the performance of a single-concept approach, which takes into account just the most relevant concept. Moreover, the experiments also prove that the proposed two-level learning hierarchy gives better performances than one of the one-level version.},
  added-at = {2010-01-29T16:44:34.000+0100},
  address = {Bled, Slovenia},
  author = {Murfi, Hendri and Obermayer, Klaus},
  biburl = {http://www.bibsonomy.org/bibtex/23b950e47ed80d1390b9bfc9f69f0f6a3/trude},
  booktitle = {ECML PKDD Discovery Challenge 2009 (DC09)},
  editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert},
  interhash = {cc156c9a9a1a3eeed967892433140a2d},
  intrahash = {3b950e47ed80d1390b9bfc9f69f0f6a3},
  issn = {1613-0073},
  keywords = {2009 ECML09 _todo recommendation social tagging},
  month = {September},
  pages = {201--214},
  publisher = {CEUR Workshop Proceedings},
  timestamp = {2010-01-29T16:44:34.000+0100},
  title = {A Two-Level Learning Hierarchy of Concept Based Keyword Extraction for Tag Recommendations},
  url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/},
  volume = 497,
  year = 2009
}