\chapter{Introduction}
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\section{The objective}
This PhD thesis deals with the study of new paradigms for exploiting data obtained from virtual and real digital traces (i.e. from Social Networks, Location-Based Social Networks, remote sensing and crowd-sourcing, physic sensors) and human factors (i.e. profiling, behavioral aspects such as movements, voice and contextual information) as a basis for the design and implementation of innovative infovis technologies, multimedia recommendation and browsing systems, human computer interaction paradigms in real and virtual spaces, i.e. in online, outdoor and indoor environments. Primary objectives have been the automatic identification of functional areas within cities through the analysis of geolocated social media information and user profiling, and the detection of macro and micro events happening in urban areas. The analysis on user generated data and the profiling of user behaviours, actual such as movements and voice interaction and implicit or psychological such as self-esteem and saliency perception have also led to research on how to exploit user profiling on online platforms and human factors to improve recommendations systems on general purpose and contextual social networks. The same approach has also been used for real locations in designing smart and personalized systems to be used in indoor, real and virtual environments. 
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\section{Contributions}
New methods for identification of functional areas in cities and for the detection of geo-localized micro and macro-events are described respectively in Chapter~\ref{chap:urban-computing-fa} and Chapter~\ref{chap:urban-computing-ed}. Using these informations as a basis, personalized recommendation and routing systems as well as immersive browsing interfaces has been created that could present innovative paradigms of human computer interaction through original solutions for information visualizations. In particular it has been developed 1) a framework which exploits user profiling on social networks and data from mobile device sensors that allows the semi-automatic recommendation and assisted definition of personalized itineraries, described in Chapter~\ref{chap:roadie}a nd 2) a framework for navigating, on the web, immersive urban routes through continuous spherical images transition, which allows the fruition of multimedia materials associated to Point-Of-Interests, described in Chapter~\ref{chap:spherical-panorama}. These systems exploit in part the techniques of user profiling presented in the first Chapter and propose and provide original solutions for information visualization. Proposed strategies of recommendation of multimedia material combined with the previously cited user profiling techniques have then been used for proposing 1) a recommender which makes an improvement to standard user and item-based recommendation algorithms, described in~\ref{chap:online-recomm}. The recommender has been implemented in a Social Network for video sharing and it is based on content-based techniques for the analysis of video annotations (the improvement has been achieved through an original exploitation of human factors, i.e. users' self-expression in user profiles and perception of visual saliency in video frames); 2) effective methods of recommendation and profiling designed for contextual SNs which exploits online profiling, collaborative filtering and information derived from the context, see Chapter~\ref{chap:contextual-sn}. New ways of recommending, browsing and navigating multimedia content in indoor real and virtual environments relying on context understanding, behaviour and interests profiling, user localisation through portable devices, ibeacon sensors and computer vision, and natural interaction have been designed and implemented in real applications in original ways. Some of these experiences and solutions are described in Chapter~\ref{chap:smart},~\ref{chap:imaging-900}~\ref{chap:deep-artwork},~\ref{chap:locomotion} and ~\ref{chap:natural-experiences}. Main objectives of these systems were the personalization of content and the understanding of the user context, preferences and behaviors in order to provide a more natural and targeted user experience.

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