Jae-wook Ahn

Open user profiles for adaptive news systems: help or harm?

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.

A Task Taxonomy for Network Evolution Analysis

Visualization has proven to be a useful tool for understanding network structures. Yet the dynamic nature of social media networks requires powerful visualization techniques that go beyond static network diagrams. To provide strong temporal network visualization tools, designers need to understand what tasks the users have to accomplish. This paper describes a taxonomy of temporal network visualization tasks. We identify the 1) entities, 2) properties, and 3) temporal features, which were extracted by surveying 53 existing temporal network visualization systems. By building and examining the task taxonomy, we report which tasks are well covered by existing systems and make suggestions for designing future visualization tools. The feedback from 12 network analysts helped refine the taxonomy.

Adaptive visualization for exploratory information retrieval

As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information.

Adaptive visualization of search results: bringing user models to visual analytics

Adaptive visualization is a new approach at the crossroads of user modeling and information visualization. Taking into account information about a user, adaptive visualization attempts to provide user-adapted visual presentation of information. This paper proposes Adaptive VIBE, an approach for adaptive visualization of search results in an intelligence analysis context. Adaptive VIBE extends the popular VIBE visualization framework by infusing user model terms as reference points for spatial document arrangement and manipulation. We explored the value of the proposed approach using data obtained from a user study. The result demonstrated that user modeling and spatial visualization technologies are able to reinforce each other, creating an enhanced level of user support. Spatial visualization amplifies the user models ability to separate relevant and non-relevant documents, whereas user modeling adds valuable reference points to relevance-based spatial visualization.

Comprehensive personalized information access in an educational digital library

This paper explores two ways to help students locate most relevant resources in educational digital libraries. One method gives a more comprehensive access to educational resources, through multiple pathways of information access, including browsing and information visualization. The second method is to access personalized information through social navigation support. This paper presents the details of the Knowledge Sea II system for comprehensive personalized access to educational resources and also presents the results of a classroom study. The study delivered a convincing argument for the importance of providing multiple information presentation modes, showing that only about 10% of all resource accesses were made through the traditional search interface. We have also collected some solid evidence in favor of the social navigation support

Temporal visualization of social network dynamics: prototypes for nation of neighbors

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.

Adaptive Knowledge-Based Visualization for Accessing Educational Examples

A number of research teams are working to organize personalized access to the modern repositories of educational resources. The goal of personalized access is to help students locate resources that match their individual goals, interests, and current knowledge. The project presented in this paper is focused on the least explored way of personalized access - adaptive visualization. Here, we present the NavEx ADVISE visualization system, which provides personalized access to a repository of educational examples. The system combines spatial, similarity-based visualization with adaptive annotations of resources. The spatial layout and the adaptive annotations are generated using a knowledge-based indexing of examples with domain concepts

Social Search in the Context of Social Navigation

The explosive growth of Web-based educational resources requires a new approach for accessing relevant information effectively. Social searching in the context of social navigation is one of several answers to this problem, in the domain of information retrieval. It provides users with not merely a traditional ranked list, but also with visual hints which can guide users to information provided by their colleagues. A personalized and context-dependent social searching system has been implemented on a platform called KnowledgeSea Ⅱ, an open-corpus Web-based educational support system with multiple access methods. Validity tests were run on a variety of aspects and results have shown that this is an effective way to help users access relevant, essential information.

Visual Analysis of Temporal Trends in Social Networks Using Edge Color Coding and Metric Timelines

We present Net EvViz, a visualization tool for analysis and exploration of a dynamic social network. There are plenty of visual social network analysis tools but few provide features for visualization of dynamically changing networks featuring the addition or deletion of nodes or edges. Our tool extends the code base of the Node XL template for Microsoft Excel, a popular network visualization tool. The key features of this work are (1) The ability of the user to specify and edit temporal annotations to the network components in an Excel sheet, (2) See the dynamics of the network with multiple graph metrics plotted over the time span of the graph, called the Timeline, and (3) Temporal exploration of the network layout using an edge coloring scheme and a dynamic Time slider. The objectives of the new features presented in this paper are to let the data analysts, computer scientists and others to observe the dynamics or evolution in a network interactively. We presented Net EvViz to five users of Node XL and received positive responses.

Towards Fully Distributed and Privacy-Preserving Recommendations via Expert Collaborative Filtering and RESTful Linked Data

Expert Collaborative Filtering is an approach to recommender systems in which recommendations for users are derived from ratings coming from domain experts rather than peers. In this paper we present an implementation of this approach in the music domain. We show the applicability of the model in this setting, and show how it addresses many of the shortcomings in traditional Collaborative Filtering such as possible privacy concerns. We also describe a number of technologies and an architectural solution based on REST and the use of Linked Data that can be used to implement a completely distributed and privacy-preserving recommender system.