\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 are revealed unintentionally (as it happens for example through the records of website visits and queries on search engines). Digital traces can be defined as metadata, i.e. data that describe the content itself so that its semantic value can be contextualized. Whether we consider the metadata produced using our personal devices, for example by writing a message on a social network that can be enriched through geolocation, timestamp, message content analysis; or those that identify our actions captured by external devices, such as a video-surveillance camera, or worn devices in a precise environment (e.g. exploiting localization, user attention and preference estimation through computer vision, 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 that just reveal what we are doing and how we act in public.\\

Today much of our activities, professional and private, are mediated by information systems. This means that the massive amount of digital footprints that are produced can be used to gain insights on people behaviors and to adapt technologies to personal preferences and needs. This can be done both at the macroscopic and microscopic levels and using the different paradigms in which digital technologies can be exploited, i.e. in online, outdoor, indoor or virtual environments. With macroscopic level we mean, for example, to process user-generated information in order to understand social and urban dynamics, or to detect and to predict events that could be of interest for the community itself or, for example, for city planners. With microscopic level instead we reference all those systems for the fruition and recommendation of content that take into account personal traces and are directed specifically to the use of individuals in `spaces'. In this context a new breed of interactive systems can be designed that can help to bridge the gap between users of applications, the multimedia content and the world itself where users move in. These systems can be used through our personal devices in online environments, where our digital representations are nowadays the mirror of the real ones (think about our social network profiles), but also in outdoor and indoor environments, or exploiting new technologies such as virtual reality systems and natural interaction. An important aspect discussed in this thesis is the investigation of how digital traces can be exploited at their best considering at the same time user preferences and behaviors, the context of use of technologies and their objective, that can be of the most varied. Human factors are in fact related to the context of use and come into play on a personal and social level when people use digital systems. Through several examples it is demonstrated how user interests, perception, self-esteem, user status and conditions, contextual needs, in particular and located situations, can be a means to better define digital traces and to improve digital systems in terms of targeted services, usability of applications and users expectations. \\


\section{Summary}
The thesis has 12 chapters, which are briefly summarized below. Chapter~\ref{chap:urban-computing-fa} and ~\ref{chap:urban-computing-ed} are dedicated to the exploitation of large amount of personal data from social networks to extract knowledge that can be valuable, at the macroscopic level, to have insights on urban dynamics.\\
\textbf{Chapter~\ref{chap:urban-computing-fa}} presents a system for the automatic detection of functional areas in cities using geolocated data from Facebook. The main idea is to characterize points of interests in urban areas through the analysis of profiles of people who have been in those POIs (i.e. restaurants, theaters, cinemas, gardens, public buildings and so on). It is demonstrated how this technique in feature selection improves the detection and gives better insights on activities that take place in POIs compared to standard approaches. A recommendation system for outdoor urban exploration that benefits from these results is also proposed.\\
\textbf{Chapter~\ref{chap:urban-computing-ed}} proposes a method for geolocated event detection in urban areas exploiting personal messages on Twitter. The method was designed to provide a tool that could be exploited and lightweight enough for identifying and predicting events by realtime systems. Recommendation systems of events in outdoor scenarios may profitably use this method.\\
In \textbf{Chapter~\ref{chap:roadie}} an application for semi-automatic recommendation of itineraries and for itinerary planning in urban scenarios is described. The Chapter suggests some techniques of user profiling from social networks and through mobile devices sensors analysis that could improve personalization with respect to traditional approaches.\\
To conclude the part dedicated to outdoor environments,  \textbf{Chapter~\ref{chap:spherical-panorama}} presents a multimedia system that allows to experience an urban itinerary remotely. The App is immersive and was conceived with the aim of implementing a system based on spherical images that would allow fluid and lightweight navigation and provide an innovative paradigm for the fruition of multimedia content.\\
\textbf{Chapter~\ref{chap:online-recomm}} and \textbf{Chapter~\ref{chap:contextual-sn}} deal with the exploitation of personal data and human factors in order to create personal recommendations in virtual communities. Compared to the macroscopic level of \textbf{Chapter~\ref{chap:online-recomm}} which tackles themes related to recommendations on general purpose online communities, \textbf{Chapter~\ref{chap:contextual-sn}} is more focused and the scope of investigation is targeted to the specificity of a contextual social network. \\
In details, \textbf{Chapter~\ref{chap:online-recomm}} proposes a method of recommendation on a video sharing platform that takes into account, in particular, perception  and desire to self-representation in order to improve user tagging and video content representation.\\
\textbf{Chapter~\ref{chap:contextual-sn}} moves the attention to the context in which a virtual community can grow (i.e. it is a social network designed for an airport area) and factors related to the site and the specific purpose of the application. Hints are given on which elements (preferences on social networks, collaborative filtering, user behaviors) can be exploited and how in order to improve user profiling, recommendation of other users and resources, and fruition of multimedia content. Results are generalizable and applicable to other similar contextual situations.
