UMAP 2017 THUM Workshop Chairs' Welcome & Organization

Abstract

The importance of user modeling and personalization is taken for granted in several scenarios. According to this widespread paradigm, each user can be modeled through some (explicitly or implicitly gathered) information about her knowledge or about her preferences, in order to adapt the behavior of a generic intelligent system to her specific characteristics. However, the recent spread of social network and self-tracking devices has totally changed the rules for personalization. On one side, the spread of social network platforms radically changed and renewed many consolidated behavioral paradigms. Thanks to the heterogeneous nature of the discussions that take place on social networks, a lot of new data are continuously available and can be gathered and exploited to build richer and more complete user models, to discover latent communities, to infer information about users' emotions and personality traits, and also to study very complex phenomena, such as those related to the psycho-social sphere, in a totally new way. At the same time, self-tracking devices are becoming more and more pervasive, and a plethora of personal data is today available by exploiting such tools. These devices model and track a lot of signals that pure content-based information which is commonly spread on social networks can't actually handle. Reasoning on these data can enable predictions about the user's behavior, health, and goals. As a consequence, it is very important to think about a new generation of user models that are able to effectively merge the information coming from both information sources, while also taking into account the fact that user models evolve over time.