A two-level learning hierarchy of concept based keyword extraction for tag recommendations

Hendri Murfi, Klaus Obermayer

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)


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 two-level 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.

Original languageEnglish
Pages (from-to)201-214
Number of pages14
JournalCEUR Workshop Proceedings
Publication statusPublished - 1 Dec 2009
EventInternational Workshop on Discovery Challenge, DC 2009 at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2009 - Bled, Slovenia
Duration: 7 Sep 20097 Sep 2009


  • Concept extraction
  • Keyword extraction
  • Machine learning
  • Recommender system
  • Social tagging


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