The primary contributions of this dissertation are to introduce a framework that can be used to design information seeking interfaces and to demonstrate that interfaces informed by this framework can improve the information seeking experience. The AgileViews framework is based on the natural and intuitive concept of views. Just as we instinctually and continually shift our focus of attention from one source of information to another to accomplish tasks and respond to stimuli in the physical world, the AgileViews framework suggests that information seeking interfaces can be improved by presenting digital information in structured, intuitive views (overviews, previews, history views, shared views, primary views, and peripheral views) and enabling users effortless interaction between these views. The practical use of this framework is illustrated in this dissertation through examples of three different prototype systems designed with the AgileViews framework.
The AgileViews framework was evaluated by conducting a two-phase user study with 28 participants. Results from the study showed that an interface developed according to the AgileViews framework does improve the user experience during information seeking, as evidenced from both objective, quantitative data and from more subjective participant impressions. Specifically, the results demonstrate that an AgileViews interface can increase the navigational efficiency and the satisfaction of people when using an information seeking system, while also encouraging them to explore the system and to be more thorough in their information seeking tasks.
Efron, M. & Geisler, G.(2001).Is it all about connections? Factors affecting the performance of a link-based recommender system. In Proceedings of the SIGIR 2001 Workshop on Recommender Systems.
This study reports on a recent evaluation of the similarity model used by Recommendation Explorer, an automatic recommender system. In particular, we consider the role of several system-internal factors in determining the quality of recommendation. More generally, we discuss factors in the recommendation task itself that complicate the construction and evaluation of recommender systems, and reflect on the implications of our findings for research in this area.
Efron, M. & Geisler, G.(2001).Using dimensionality reduction to improve similarity judgements for recommendation. In Proceedings of the Second DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries.
Recommendation Explorer is an experimental recommender system that attempts to address the provisional and contextual nature of user information needs by coupling the system's interface and recommendation algorithms. This study reports on the development and evaluation of a new similarity module for RecEx. Based on the dimensionality reduction via the Singular Value Decomposition (SVD), the new module discovers high-order relationships among database items, thus obtaining a robust model of item-item similarity.