Minimal Knowledge Anonymous User Profiling for Personalized Services

An algorithmic and formal method is presented for automatic profiling of anonymous internet users. User modelling represents a relevant problem in most internet successful user services, such as news sites or search engines, where only minimal knowledge about the user is given, i.e. information such as user session, user tracing and click-stream analysis is not available. On the other hand the ability of giving a personalised response, i.e. tailored on the user preferences and expectations, represents a key factor for successful online services. The proposed model uses the notion of fuzzy similarities in order to match the user observed knowledge with appropriate target profiles. We characterize fuzzy similarity in the theoretical framework of Lukasiewicz structures which guaranties the formal correctness of the approach. The presented model for user profiling with minimal knowledge has many applications, from generation of banners for online advertising to dynamical response pages for public services.