Urbanization’s rapid progress has changed and modernized citizen’s lives but also raised big challenges, such as traffic congestion, energy consumption, and pollution. A few years ago, tackling these challenges seemed impossible considering the complex and dynamic settings of cities. However, nowadays new sensing technologies and large-scale computing infrastructures allow to produce and collect a great variety of data in urban spaces, allowing to obtain rich knowledge about cities. Such data include different kinds of sensors and devices installed in buildings, vehicles and artefacts and also wearable and mobile devices held by humans. In this regard, urban computing aims at leveraging on the knowledge provided by the data collected in urban spaces to solve major issues faced today by our cities. The final goal is to understand the nature of urban phenomena, and even predict the future of cities. To that end, it connects urban sensing, data acquisition, data management, data analytics, data visualization and service providing into an unobtrusive and continuous process aiming at improving citizens’ lives, human life quality, city operation systems, and the environment.

Particularly, urban computing can be used to build user models aiming at providing citizens with personalized services. This has important implications in the context of inclusive eGovernment and Smart Cities, which could leverage on the user’s models to design and tailor services according to the characteristics and needs of each particular citizen. User modeling and personalization are commonly used in multiple tasks, in which users are characterized only based on explicit information about their knowledge, behaviour, social relations or preferences, aiming at adapting generic systems to the particularities of each user. The ubiquitousness of social networking sites, and mobile and smart-devices offer new information sources, opportunities and challenges for changing the personalization paradigm. This includes mining and analysing user behaviour aiming at better understanding users (and ourselves), and thereby create more accurate models and personalisation strategies. The analysis of these new data sources offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, health, psychology, ecology, economy, information and computer sciences, or education.