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.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00913884
Contributor : Modou Gueye <>
Submitted on : Wednesday, December 4, 2013 - 2:57:39 PM
Last modification on : Thursday, October 17, 2019 - 12:36:06 PM

Links full text

Identifiers

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

Citation

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

Share

Metrics

Record views

263