A Two-Level Learning Hierarchy of Concept Based Keyword Extraction for Tag Recommendations
H. Murfi, and K. Obermayer. ECML PKDD Discovery Challenge 2009 (DC09), 497, page 201--214. Bled, Slovenia, CEUR Workshop Proceedings, (September 2009)
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.
%0 Conference Paper
%1 marinho:ecml2009
%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 ECML09 _todo recommendation social tagging
%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{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 = {https://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
}