On the Web, users typically forage for information by navigating from page to page along Web links. Their surfing patterns or actions are guided by their information needs. Researchers need tools to explore the complex interactions between user needs, user actions, and the structures and contents of the Web. In this paper, we describe two computational methods for understanding the relationship between user needs and user actions. First, for a particular pattern of surfing, we seek to infer the associated information need. Second, given an information need, and some pages as starting pints, we attempt to predict the expected surfing patterns. The algorithms use a concept called “information scent”, which is the subjective sense of value and cost of accessing a page based on perceptual cues. We present an empirical evaluation of these two algorithms, and show their effectiveness.

Designers and researchers of users' interactions with the World Wide Web need tools that permit the rapid exploration of hypotheses about complex interactions of user goals, user behaviors, and Web site designs. We present an architecture and system for the analysis and prediction of user behavior and Web site usability. The system integrates research on human information foraging theory, a reference model of information visualization and Web data-mining techniques. The system also incorporates new methods of Web site visualization (Dome Tree, Usage Based Layouts), a new predictive modeling technique for Web site use (Web User Flow by Information Scent, WUFIS), and new Web usability metrics.

Digital books can significantly enhance the reading experience, providing many functions not available in printed books. In this paper we study a particular augmentation of digital books that provides readers with customized recommendations. We systematically explore the application of spreading activation over text and citation data to generate useful recommendations. Our findings reveal that for the tasks performed in our corpus, spreading activation over text is more useful than citation data. Further, fusing text and citation data via spreading activation results in the most useful recommendations. The fused spreading activation techniques outperform traditional text-based retrieval methods. Finally, we introduce a preliminary user interface for the display of recommendations from these algorithms.

Several visualizations have emerged which attempt to visualize all or part of the World Wide Web.Those visualizations, however, fail to present the dynamically changing ecology of users and documents on the Web.We present new techniques for Web Ecology and Evolution Visualization (WEEV).Disk Trees represent a discrete time slice of the Web ecology.A collection of Disk Trees forms a Time Tube, representing the evolution of the Web over longer periods of time.These visualizations are intended to aid authors and webmasters with the production and organization of content, assist Web surfers making sense of information, and help researchers understand the Web.

To facilitate users' ability to make sense of large collections of hypertext we present two new techniques for inducing clusters of related documents on the World Wide Web. Users' ability to find relevant information might also be enhanced by finding lawful properties of document behavior and use. We present models and analyses of document use and change for the World Wide Web. Keywords Clustering, categorization, co-citation analysis, World Wide Web, hypertext, survival analysis, usage models INTRODUCTION The ever-increasing universe of electronic information competes for the effectively fixed and limited attention of people. Both consumers and producers of information want to understand what kinds of information are out there, how desirable it is, and how its content and use change through time. Our work aims to discover empirical regularities of hypertext content, use, and structure, and ways of exploiting these regularities to provide new ways of helping people to find and make...

In its current implementation, the World-Wide Web lacks much of the explicit structure and strong typing found in many closed hypertext systems.While this property probably relates to the explosive acceptance of the Web, it further complicates the already difficult problem of identifying usable structures and aggregates in large hypertext collections.These reduced structures, or localities, form the basis for simplifying visualizations of and navigation through complex hypertext systems.Much of the previous research into identifying aggregates utilize graph theoretic algorithms based upon structural topology, i.e., the linkages between items.Other research has focused on content analysis to form document collections.This paper presents our exploration into techniques that utilize both the topology and textual similarity between items as well as usage data collected by servers and page meta-information lke title and size.Linear equations and spreading activation models are employed to arrange Web pages based upon functional categories, node types, and relevancy.

Network-accessible multimedia databases, repositories, and libraries are proliferating at a rapid rate. A crucial problem for these repositories remains timely and appropriate document access. In this article, we borrow a model from psychological research on human memory, which has long studied retrieval of memory items based on frequency and recency rates of past item occurrences. Specifically, the model uses frequency and recency rates of prior document accesses to predict future document requests. The model is illustrated by analyzing the log file of document accesses to the Georgia Institute of Technology World Wide Web (WWW) repository, a large multimedia respository exhibiting high access rates. Results show that the model predicts document access rates with a reliable degree of accuracy. We describe extensions to the basic approach that combine the recency and frequency analyses and which incorporate respository structure and document type. These results have implications for the formulation of descriptive user models of information access in large repositories. In addition, we sketch applications in the areas of design of information systems and interfaces and their document-caching algorithms.