Items by Silvestri, Fabrizio

Giving suggestions to users of Web-based services is a common practice aimed at enhancing their navigation experience. Major Web Search Engines usually provide Suggestions under the form of queries that are, to some extent, related to the current query typed by the user, and the knowledge learned from the past usage of the system. In this work we introduce Search Shortcuts as “Successful ” queries allowed, in the past, users to satisfy their information needs. Differently from conventional suggestion techniques, our search shortcuts allows to evaluate effectiveness by exploiting a simple train-and-test approach. We have applied several Collaborative Filtering algorithms to this problem, evaluating them on a real query log data. We generate the shortcuts from all user sessions belonging to the testing set, and measure the quality of the shortcuts suggested by considering the similarity between them and the navigational user behavior.