Abstract not found

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 points, 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.

Much of software developers’ time is spent understanding unfamiliar code. To better understand how developers gain this understanding and how software development environments might be involved, a study was performed in which developers were given an unfamiliar program and asked to work on two debugging tasks and three enhancement tasks for 70 minutes. The study found that developers interleaved three activities. They began by searching for relevant code both manually and using search tools; however, they based their searches on limited and misrepresentative cues in the code, environment, and executing program, often leading to failed searches. When developers found relevant code, they followed its incoming and outgoing dependencies, often returning to it and navigating its other dependencies; while doing so, however, Eclipse’s navigational tools caused significant overhead. Developers collected code and other information that they believed would be necessary to edit, duplicate, or otherwise refer to later by encoding it in the interactive state of Eclipse’s package explorer, file tabs, and scroll bars. However, developers lost track of relevant code as these interfaces were used for other tasks, and developers were forced to find it again. These issues caused developers to spend, on average, 35 percent of their time performing the mechanics of navigation within and between source files. These observations suggest a new model of program understanding grounded in theories of information foraging and suggest ideas for tools that help developers seek, relate, and collect information in a more effective and explicit manner.

According to usability experts, the top user issue for Web sites is difficult navigation. We have been developing automated usability tools for several years, and here we describe a prototype service called InfoScent^TM Bloodhound Simulator, a push-button navigation analysis system, which automatically analyzes the information cues on a Web site to produce a usability report. We further build upon previous algorithms to create a method called Information Scent Absorption Rate, which measures the navigability of a site by computing the probability of users reaching the desired destinations on the site. Lastly, we present a user study involving 244 subjects over 1385 user sessions that show how Bloodhound correlates with real users surfing for information on four Web sites. The hope is that, by using a simulation of user surfing behavior, we can reduce the need for human labor during usability testing, thus dramatically lower testing costs, and ultimately improving user experience. The Bloodhound Project is unique in that we apply a concrete HCI theory directly to a real-world problem. The lack of empirically validated HCI theoretical model has plagued the development of our field, and this is a step toward that direction.

SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT architecture) has been developed to simulate users as they perform unfamiliar information-seeking tasks on the World Wide Web (WWW). SNIF-ACT selects actions based on the measure of information scent, which is calculated by a spreading activation mechanism that captures the mutual relevance of the contents of a WWW page to the goal of the user. There are two main predictions of SNIF-ACT: (1) users working on unfamiliar tasks are expected to choose links that have high information scent, (2) users will leave a site when the information scent of the site diminishes below a certain threshold. SNIF-ACT produced good fits to data collected from four users working on two tasks each. The results suggest that the current content-based spreading activation SNIF-ACT model is able to generate useful predictions about complex user-WWW interactions.

Eye tracking studies of the Hyperbolic Tree browser suggest that visual search in focus+context displays is highly affected by information scent (i.e., the proximal cues to the value of distal information). When users detected a strong information scent, they were able to reach their goal faster with the Hyperbolic Tree browser than with a conventional browser. When users detected a weak scent or no scent, they were less efficient in visually searching the areas of the Hyperbolic display that had a higher density of visual items. Two countervailing processes appear to affect visual attention in the Hyperbolic display: (1) Strong information scent expands the spotlight of attention, whereas (2) the crowding of targets in a compressed region narrows it. We interpret these results through an integration of the CODE Theory of Visual Attention with information foraging theory. Keywords Focus+context, Hyperbolic Tree, information foraging, information scent, information visualization, visu...

Previous studies for menu and Web search tasks have suggested di#ering advice on the optimal number of selections per page. In this paper, we examine this discrepancy through the use of a computational model of information navigation that simulates users navigating through a Web site. By varying the quality of the link labels in our simulations, we find that the optimal structure depends on the quality of the labels and are thus able to account for the results in the previous studies. We present additional empirical results to further validate the model and corroborate our findings. Finally we discuss our findings' implications for the information architecture of Web sites.

Introduction Integrating theory, data, and knowledge about cognitive psychology and human performance in a way that is useful for guiding design in HCI is still not a simple matter. However, there have been significant advances since Card, Moran, and Newell wrote the above passage. One of the key advances is the development of cognitive architecture, the subject of this chapter. The chapter will first consider the what it is to be cognitive architecture and why cognitive architecture is relevant for HCI. In order to detail the present state of cognitive architectures in HCI, it is important to consider some of the past use of cognitive architectures in HCI research. Then, four architectures actively in use in the research community (LICAI/CoLiDeS, Soar, EPIC, and ACT-R/ PM) and their application to HCI will be examined. The chapter will conclude with a discussion of the future of cognitive architectures in HCI. 1.1 What Are Cognitive Architectures? Most any di

The Internet has dramatically changed how people sell and buy goods. In recent years we have seen the emergence of electronic marketplaces that leverage information technology to create more efficient markets (e.g., online auctions) to bring together buyers and sellers with greater effectiveness at a massive scale. Despite the growing interest in and importance of such marketplaces, our understanding of how the design of the marketplace affects its use is still quite limited. This paper presents a detailed case study of a currently operational massive scale online auction marketplace. The main focus is to gain initial insights into the effects of the design of the marketplace. The results of the study point to several important considerations and implications not only for the design of online marketplaces but also for the design of large-scale websites where effective locating of information is key to user success. Keywords Electronic Marketplaces, Massive Scale Online Auctions, Online Market Design, Market Technostructure, Market Navigation, Item Display, Information Overload

Emerging research has sought to bring effective debugging devices to end-user programmers. This research has largely focused on how well such devices bring genuine “functional ” rewards to end users. However, emerging models of programming behavior indicate that another, often ignored, type of reward—perceivable rewards—can play an equally vital role in how well debugging devices serve end users. Using an empirically evaluated fault localization device, this paper investigates the impact such perceivable rewards can have on end-user debugging. Our results indicate that perceivable rewards alone can significantly improve the effectiveness and understanding of end users performing debugging tasks. 1.