Improving tag recommendation by folding in more consistency

TL;DR

This paper introduces a re-ranking method for tag recommendation systems that uses association rules to improve the consistency of suggested tags, showing effectiveness across multiple datasets.

Contribution

It presents a novel re-ranking approach that enhances tag recommendation consistency by mining association rules, applicable as an add-on to existing recommenders.

Findings

Improved recommendation quality demonstrated on five datasets.

Method is easily parallelizable and adaptable to various recommenders.

Re-ranking leads to more consistent tag suggestions.

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.