Hidden Markov models for context-aware tag query prediction in folksonomies
Résumé
Recently, social bookmarking systems have received surging attention in academic and industrial communities. In fact, social bookmarking systems share with the Semantic Web vision the idea of facilitating the collaborative organization and sharing of knowledge on the web. The reason for the apparent success of the upcoming tools for resource sharing (social bookmarking systems, photo sharing systems, etc.) lies mainly in the fact that no specific skills are needed for publishing and editing, and an immediate benefit is yielded to each individual user, e.g., organizing one's bookmarks in a browser-independent, persistent fashion, without too much overhead. As these systems grow larger, however, the users address the need of enhanced search facilities. Today, full-text search is supported, but the results are usually simply listed decreasingly by their upload date. The challenging research issue is, therefore, the development of a suitable prediction framework to support users in effectively retrieving the resources matching their real search intents. The primary focus of this chapter is to propose a new, context aware tag query prediction approach. Specifically, the authors adopted Hidden Markov Models and formal concept analysis to predict users' search intentions based on a real folksonomy. Carried out experiments emphasize the relevance of the proposal and open many issues