%0 Conference Paper %1 murfi2009twolevel %A Murfi, Hendri %A Obermayer, Klaus %B ECML PKDD Discovery Challenge 2009 (DC09) %C Bled, Slovenia %D 2009 %E Eisterlehner, Folke %E Hotho, Andreas %E Jäschke, Robert %I CEUR Workshop Proceedings %K 2009 extraction keyword recommendation recommender %P 201--214 %T A Two-Level Learning Hierarchy of Concept Based Keyword Extraction for Tag Recommendations %U http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/ %V 497 %X 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.

@inproceedings{murfi2009twolevel, 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 = {2010-11-03T17:03:07.000+0100}, address = {Bled, Slovenia}, author = {Murfi, Hendri and Obermayer, Klaus}, biburl = {https://www.bibsonomy.org/bibtex/23b950e47ed80d1390b9bfc9f69f0f6a3/philonous}, 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 extraction keyword recommendation recommender}, month = {September}, pages = {201--214}, publisher = {CEUR Workshop Proceedings}, timestamp = {2010-11-03T17:03:07.000+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 }