Folksonomy Query Suggestion via Users’ Search Intent Prediction
AbstractRecently, social bookmarking systems have received surging an increasing attention in both academic and industrial communities. The main thrust of these Web 2.0 systems is their easy use that relies on simple intuitive process, allowing their users to label diverse resources with freely chosen keywords aka tags. The obtained collection are known under the nickname Folksonomy. 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. Challenging research issue is therefore the development of suitable prediction framework to support users in effectively retrieving the resources matching their real search intents. The primary focus of this paper is to propose a new users’ search intent prediction approach for query tag suggestion. Specifically, we adopted Hidden Markov Models and triadic concept analysis to predict users’ search intents in a folksonomy. Carried out experiments emphasize the relevance of our proposal and open many issues.KeywordsHidden Markov Models folksonomies Search intentTriadic concepts analysis