\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 as regard to real locations in designing smart and personalized systems to be used in indoor 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 also 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, cf. 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. Insights from 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.
}

\section{Digital Traces}

Physical traces that humans leave on things in the world are everywhere. They can be visible or imperceptible, left intentionally or unintentionally. Just think of footprints on a floor, a lost hair 
in the bed or between the pillows of the couch, or a note that we have written and abandoned somewhere to remind us of something. As regard to invisible traces, we can consider the fingerprints that we leave on anything we touch with bare hands. All these traces are essential information that reveal where we have been, how we got there, objects we have handled, with whom we have been, and they can say a lot even with respect to our interests and preferences. This is no different in the digital world, where it happens on an even larger scale. We all, aware or unaware, leave hundreds and hundreds of digital traces every day working at our computers (sending emails, writing blog posts, interacting on social networks through Twitter messages, Facebook status updates, Youtube videos views and uploads, etc.) or simply carrying around our mobile phones with us and using applications (through logs of device sensor data such as GPS, accelerometers, camera, microphone etc.). Through these logs it is possible to extract information that we would assume to be private and that can instead give detailed insights of our lives that we have revealed unintentionally (as it happens for example through records of our website visits and queries on search engines). Digital traces can be defined as metadata because they are data that describe the content itself so that its semantic value can be contextualized. Whether we consider the metadata produced by our personal devices, for example by writing a message on a social network that can be enriched using geolocation, timestamp, message content analysis; or those that identify our actions captured by digital devices in a precise environment (e.g. exploiting localization or user attention and preference estimation through a camera, voice recognition, activity detection), all these are information that have to do a lot with who we are, how we represent ourselves in online or virtual worlds or just how we act in public.