Over the last five years, a range of projects have focused on progressively more elaborated techniques for adaptive news delivery. However, the adaptation process in these systems has become more complicated and thus less transparent to the users. In this paper, we concentrate on the application of open user models in adding transparency and controllability to adaptive news systems. We present a personalized news system, YourNews, which allows users to view and edit their interest profiles, and report a user study on the system. Our results confirm that users prefer transparency and control in their systems, and generate more trust to such systems. However, similar to previous studies, our study demonstrate that this ability to edit user profiles may also harm the system's performance and has to be used with caution.

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Abstract-Visualization is a useful tool for understanding the nature of networks. The recent growth of social media requires more powerful visualization techniques beyond static network diagrams. One of the most important challenges is the visualization of temporal network evolution. In order to provide strong temporal visualization methods, we need to understand what tasks users accomplish. This study provides a taxonomy of the temporal network visualization tasks. We identify (1) the entities, (2) the properties to be visualized, and (3) the hierarchy of temporal features, which were extracted by surveying existing temporal network visualization systems. By building and examining the task taxonomy, we report which tasks have been covered so far and suggest additions for designing the future visualizations. We also present example visualizations constructed using the task taxonomy for a social networking site in order to validate the quality of the taxonomy.

Personalized Web search has emerged as one of the hottest topics for both the Web industry and academic researchers. However, the majority of studies on personalized search focused on a rather simple type of search, which leaves an important research topicthe personalization in exploratory searches -as an under-studied area. In this paper, we present a study of personalization in taskbased information exploration using a system called TaskSieve. TaskSieve is a Web search system that utilizes a relevance feedback based profile, called a "task model", for personalization. Its innovations include flexible and user controlled integration of queries and task models, task-infused text snippet generation, and on-screen visualization of task models. Through an empirical study using human subjects conducting task-based exploration searches, we demonstrate that TaskSieve pushes significantly more relevant documents to the top of search result lists as compared to a traditional search system. TaskSieve helps users select significantly more accurate information for their tasks, allows the users to do so with higher productivity, and is viewed more favorably by subjects under several usability related characteristics.

Abstract. Information visualization is a powerful tool for analyzing the dynamic nature of social communities. Using Nation of Neighbors community network as a testbed, we propose five principles of implementing temporal visualizations for social networks and present two research prototypes: NodeXL and TempoVis. Three different states are defined in order to visualize the temporal changes of social networks. We designed the prototypes to show the benefits of the proposed ideas by letting users interactively explore temporal changes of social networks.