A Thin-Server Approach to Ephemeral Web Personalization Exploiting RDF Data Embedded in Web Pages

Over the last years adaptive Web personalization has become a widespread service and all the major players of the WWW are providing it in various forms. Ephemeral personalization, in particular, deals with short time interests which are often tacitly entailed from user browsing behaviour or contextual information. Such personalization can be found almost anywhere in the Web in several forms, ranging from targeting advertising to automatic language localisation of content. In order to present personalized content a user model is typically built and maintained at server-side by collecting, explicitly or implicitly, user data. In the case of ephemeral personalization this means storing at server-side a huge amount of user behaviour data, which raises severe privacy concerns. The evolution of the semantic Web and the growing availability of semantic metadata embedded in Web pages allow a role reversal in the traditional personalization scenario. In this paper we present a novel approach towards ephemeral Web personalization consisting in a clientside semantic user model built by aggregating RDF data encountered by the user in his/her browsing activity and enriching them with triples extracted from DBpedia. Such user model is then queried by a server application via SPARQL to identify a user stereotype and finally address personalized content.