STaR: a Social Tag Recommender System

The continuous growth of collaborative platforms we are recently witnessing made possible the passage from an 'elitary' Web, written by few and read by many, towards the so-called Web 2.0, a more 'user-centric' vision, where users become active contributors in Web dynamics. In this context, collaborative tagging systems are rapidly emerging: in these platforms users can annotate resources they like with freely chosen keyword (called tags) in order to make retrieval of information and serendipitous browsing more and more easier. 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. The goal of these systems (called tag recommenders) is to suggest a set of relevant keywords for the resources to be annotated by exploiting different approaches. In this paper we present a tag recommender developed for the ECML-PKDD 2009 Discovery Challenge. Our approach is based on two assumptions: firstly, if two or more resources share some common patterns (e.g. the same features in the textual description), we can exploit this information supposing that they could be annotated with similar tags. Furthermore, since each user has a typical manner to label resources, a tag recommender might exploit this information to weigh more the tags she already used to annotate similar resources.