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Improving tag recommendation by folding in more consistency
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.Comment: 14 page