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...

• Type: Conference
• Year: 2010
• Where: IIR
• Authors: Cataldo Musto, Fedelucio Narducci, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro
• Keywords: Collaborative Tagging Systems, IIR 2010, Information Retrieval, Information Technology, Web 2.0