A biologically inspired model for describing the user behaviors in a Cultural Heritage environment

We present a biologically inspired mathematical model for describing the personalized interactions of users with cultural heritage objects. The main idea is to measure the interest of an artwork spectator by means of a model able to capture the context and the visitor behaviors. In the proposed approach, the user is assimilated to a computational neuron, and its interests are deduced by counting potential spike trains, generated by external currents. Preliminary experimental results based on a phantom database, built from a real scenario, are shown. Finally, comparison results and application scenarios that may be adopted for the context-aware user profiling in the cultural heritage framework are discussed.