Trust-Based User Profiling

We have introduced the notion of user profiling with trust, as a solution to the problem of uncertainty and unmanageable exposure of personal data during access, retrieval and consumption by web applications. Our solution suggests explicit modeling of trust and embedding trust metrics and mechanisms within very fabric of user profiles. This has in turn allowed information systems to consume and understand this extra knowledge in order to improve interaction and collaboration among individuals and system. When formalizing such profiles, another challenge is to realize increasingly important notion of privacy preferences of users. Thus, the profiles are designed in a way to incorporate preferences of users allowing target systems to understand privacy concerns of users during their interaction. A majority of contributions of this work had impact on profiling and recommendation in digital libraries context, and was implemented in the framework of EU FP7 Smartmuseum project. Highlighted results start from modeling of adaptive user profiles incorporating users taste, trust and privacy preferences. This in turn led to proposal of several ontologies for user and content characteristics modeling for improving indexing and retrieval of user content and profiles across the platform. Sparsity and uncertainty of profiles were studied through frameworks of data mining and machine learning of profile data taken from on-line social networks. Results of mining and population of data from social networks along with profile data increased the accuracy of intelligent suggestions made by system to improving navigation of users in on-line and off-line museum interfaces. We also introduced several trust-based recommendation techniques and frameworks capable of mining implicit and explicit trust across ratings networks taken from social and opinion web. Resulting recommendation algorithms have shown to increase accuracy of profiles, through incorporation of knowledge of items and users and diffusing them along the trust networks. At the same time focusing on automated distributed management of profiles, we showed that coverage of system can be increased effectively, surpassing comparable state of art techniques. We have clearly shown that trust clearly elevates accuracy of suggestions predicted by system. To assure overall privacy of such value-laden systems, privacy was given a direct focus when architectures and metrics were proposed and shown that a joint optimal setting for accuracy and perturbation techniques can maintain accurate output. Finally, focusing on hybrid models of web data and recommendations motivated us to study impact of trust in the context of topic-driven recommendation in social and opinion media, which in turn helped us to show that leveraging content-driven and tie-strength networks can improve systems accuracy for several important web computing tasks.