Improving tag recommendation by folding in more consistency
Information Retrieval
2013-10-01 v1
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.
Cite
@article{arxiv.1309.7517,
title = {Improving tag recommendation by folding in more consistency},
author = {Modou Gueye and Talel Abdessalem and Hubert Naacke},
journal= {arXiv preprint arXiv:1309.7517},
year = {2013}
}
Comments
14 pages