In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles.
To the best of our knowledge, the first attempt to match user identities was proposed in @cite_32 . In that paper, the authors performed an in-depth analysis of 25 adaptive systems with the goal of identifying what user profile attributes were frequently recurrent in each system; the outcome of such an analysis led to isolate attributes like username, name, location and e-mail address.
@cite_32
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Currently, there is an increasing demand for user-adaptive systems for various purposes in many different domains. Typically, personalisation in information systems occurs separately within each system. The recent trends in user modeling rely on cross-system personalisation, i.e., the opportunity to share information across multiple information systems in order to improve user adaptation. Cooperation among systems in order to exchange user model knowledge is a complex task. This paper addresses a key challenge for cross-system personalisation which is often taken as a starting assumption, i.e., user identification. In this paper, we describe the conceptualization and implementation of a framework that provides a common base for user identification for cross-system personalisation among web-based user-adaptive systems. However, the framework can be easily adopted in different working environments and for different purposes. The framework represents a hybrid approach which draws parallels both from centralized and decentralized solutions for user modeling. To perform user identification, we propose to exploit a set of identification properties that are combined using an identification algorithm.