A framework for holistic user modeling merging heterogeneous digital footprints

In this paper we introduce the concept of holistic user profile, intended as a unique representation of a user that merges the heterogeneous footprints she spread on social networks and through personal devices, and we present a framework that supports the creation of such user models. Our holistic user model is based on the insight that each person can be described through different facets, such as her interests, activities, habits, mood, social connections and so on, and each facet can be modeled by gathering and merging information coming from diverse sources. To this end, we designed a platform that automatically acquires personal data coming from both social networks, such as Twitter and Facebook, as well as devices, such as Android smartphones and FitBit wristbands. Next, these rough data are merged, processed and enriched in order to infer high-level features (e.g., user interested in technology, user experiencing a bad mood, sedentary person, and so on) or to extract user behavioral patterns (e.g., places that are frequently visited). Such unique representation of a person, whose strength is the combination of different heterogeneous data points, is finally encoded and stored in our platform and can be made available to both the user itself and to third-party services. In the former case, data are shown through a visual interface. In the latter, holistic user profiles are exposed through this unique entry point and external applications can build newpersonalized services based on the digital footprints spread by the user.