Collaborative tagging has emerged as a common solution for labelling and organising online digital content. However, collaborative tagging systems typically suffer from a number of issues such as tag scarcity or ambiguous labelling. As a result, the organisation and browsing of tagged content is far from being optimal. In this work the authors present a general scheme for building a folksonomy-based tag recommendation system to help users tagging online content resources. Based on this general scheme, the authorse describe eight tag recommendation methods and extensively evaluate them with data coming from two real-world large-scale datasets of tagged images and sound clips. Their results show that the proposed methods can effectively recommend relevant tags, given a set of input tags and tag co-occurrence information. Moreover, the authors show how novel strategies for selecting the appropriate number of tags to be recommended can significantly improve methods performances. Approaches such as the one presented here can be useful to obtain more comprehensive and coherent descriptions of tagged resources, thus allowing a better organisation, browsing and reuse of online content. Moreover, they can increase the value of folksonomies as reliable sources for knowledge-mining.

This work is partially supported under BES2010-037309 FPI grant from the Spanish Ministry of Science and Innovation for the TIN2009-14247-C02-01 DRIMS project. JS acknowledges 2009-SGR-1434 from Generalitat de Catalunya, JAEDOC069/2010 from Consejo Superior de Investigaciones Cientificas, and FP7-ICT-2011-8-318770 from the European Commission.