We present the design of a new social bookmark manager, named GalViz, as part of the interface of the GiveA-Link system. Unlike the interfaces of traditional social tagging tools, which usually display information in a list view, GalViz visualizes tags, resources, social links, and social context in an interactive network, combined with the tag cloud. Evaluations through a scenario case study and log analysis provide evidence of the effectiveness of our design.
With the emergence of social tagging systems and the possibility for users to extensively annotate web resources and any content enormous amounts of unordered information and user generated metadata circulate the Web. Accordingly a viable visualisation form needs to integrate this unclassified content into meaningful visual representations. We argue that tag clouds can make the grade. We assume that the application of clustering techniques for arranging tags can be a useful method to generate meaningful units within a tag cloud. We think that clustered tag clouds can potentially help to enhance user performance. In this paper we present a description of tag clouds including a theoretical discourse on the strengths and weaknesses of using them in common Web-based contexts. Further recent methods of semantic clustering for visualizing tag clouds are reviewed. Findings from user studies that investigated the visual perception of differently arranged depictions of tags follow. The main objective consists in the exploration of characteristical aspects in perceptual phenomenons and cognitive processes during the interaction with a tag cloud. This clears the way for useful implications on the constitution and design factors of that visualisation form. Finally a new approach is proposed in order to further develop on this concept.  Social bookmarking tools are very popular nowadays. In most tools, users tag the bookmarks to describe them. Therefore, it is of- ten hard for users to discover implicit structures between tags, users and bookmarks. We think that this is essential for both end users to discover new bookmarks that could be of interest to them, and for researchers who want to study how people use social information re- trieval tools. In this work, a cluster map visualisation technique is customized to enable users to explore social bookmarks in the del.icio.us and the CALIBRATE system. The design of our visualisation aims to automatically identify tag and community structures, and visualises these structures in order to increase the users awareness of them.  Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.  In this paper we discuss the use of clustering techniques to enhance the user experience and thus the success of collaborative tagging services. We show that clustering techniques can improve the user experience of current tagging services. We first describe current limitations of tagging services, second, we give an overview of existing approaches. We then describe the algorithms we used for tag clustering and give experimental results. Finally, we explore the use of several techniques to identify semantically related tags.  In this paper, our aim is to facilitate synchronous and co-present interaction with social bookmarking systems for groups of related users meeting to discuss and share their collections of tags and bookmarks. Our work results in a system called Orchis that proposes a graphical user interface based on cooperative visualization and interaction as an alternative graphical user interface for social bookmarking systems. Orchis presents three major characteristics: (1) graphical overviews of collections of annotated bookmarks and tags, (2) advanced drag-and-drop interaction styles adaptable to distributed display environments and (3) support for distributed architectures possibly running different windowing systems. Our hypothesis is that by using Orchis, related users will be able to better compare and share tags and bookmarks. They will also be able to build cooperatively valuable shared collections. We expect that, in turn, this will participate in improving the overall quality of both folksonomies and social bookmarking collections. 