An IR-Based Approach for Tag Recommendation

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An IR-Based Approach for Tag Recommendation
Thanks to the continuous growth of collaborative platforms like YouTube, Flickr and Delicious, we are recently witnessing to a rapid evolution of web dynamics towards a more `social' vision, called Web 2.0. In this context collaborative tagging systems are rapidly emerging as one of the most promising tools. However, as tags are handled in a simply syntactical way, collaborative tagging systems suffer of typical Information Retrieval (IR) problems like polysemy and synonymy: so, in order to reduce the impact of these drawbacks and to aid at the same time the so-called tag convergence, systems that assist the user in the task of tagging are required. In this paper we present a system, called STaR, that implements an IR-based approach for tag recommendation. Our approach, mainly based on the exploitation of a stateof-the-art IR-model called BM25, relies on two assumptions: firstly, if two or more resources share some common patterns (e.g. the same features in the textual descriptio...
Cataldo Musto, Fedelucio Narducci, Marco de Gemmis
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2010
Where IIR
Authors Cataldo Musto, Fedelucio Narducci, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro
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