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Improving tag recommendation by folding in more consistency

Abstract : Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for tagging an item. In this paper we present a part of our work in progress which is a novel improvement of recommendations by re-ranking the output of a tag recommender. We mine association rules between candidates tags in order to determine a more consistent list of tags to recommend. Our method is an add-on one which leads to better recommendations as we show in this paper. It is easily parallelizable and morever it may be applied to a lot of tag recommenders. The experiments we did on five datasets with two kinds of tag recommender demonstrated the efficiency of our method.
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Preprints, Working Papers, ...
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Contributor : Modou Gueye Connect in order to contact the contributor
Submitted on : Wednesday, December 4, 2013 - 2:57:39 PM
Last modification on : Sunday, June 26, 2022 - 9:45:57 AM

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  • HAL Id : hal-00913884, version 1
  • ARXIV : 1309.7517


Modou Gueye, Talel Abdessalem, Hubert Naacke. Improving tag recommendation by folding in more consistency. 2013. ⟨hal-00913884⟩



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