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)

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 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
Volume497
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

Keywords

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

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