Inspired by Hill and Hollan's original work [6], we have been developing a theory of interaction history and building tools to apply this theory to navigation in a complex information space. We have built a series of tools --- map, trails, annotations and signposts --- based on a physical-world navigation metaphor. These tools have been in use for over a year. Our user study involved a controlled browse task and showed that users were able to get the same amount of work done with significantly less effort. Keywords information navigation, information foraging, interaction history, Web browsing INTRODUCTION Digital information has no history. It comes to us devoid of the patina that forms on physical objects as they are used. In the non-digital world we make extensive use of these traces to guide our actions, to make choices, and to find things of importance or interest. We call this area interaction history; that is, the records of the interactions of people and objects. Physical o...

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.

Information foraging theory is an approach to understanding how strategies and technologies for information seeking, gathering, and consumption are adapted to the flux of information in the environment. The theory assumes that people, when possible, will modify their strategies or the structure of the environment to maximize their rate of gaining valuable information. The theory is developed by (a) adaptation (rational) analysis of information foraging problems and (b) a detailed process model (adaptive control of thought in information foraging [ACT-IF]). The adaptation analysis develops (a) information patch models, which deal with time allocation and information filtering and enrichment activities in environments in which information is encountered in clusters; (b) information scent models, which address the identification of information value from proximal cues; and (c) information diet models, which address decisions about the selection and pursuit of information items. ACT-IF is instantiated as a production system model of people interacting with complex information technology. Humans actively seek, gather, share, and consume information to a degree unapproached by other organisms. Ours might properly be characterized as a species of informavores (Dennett, 1991). Our adaptive success depends to a large extent on a vast and complex

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The current state of web search is most successful at directing users to appropriate web sites. Once at the site, the user has a choice of following hyperlinks or using site search, but the latter is notoriously problematic. One solution is to develop specialized search interfaces that explicitly support the types of tasks users perform using the information specific to the site. A new way to support task-based site search is to dynamically present appropriate metadata that organizes the search results and suggests what to look at next, as a personalized intermixing of search and hypertext.

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.

This paper proposes a transformation of the Cognitive Walkthrough (CW), a theory-based usability inspection method that has proven useful in designing applications that support use by exploration. The new Cognitive Walkthrough for the Web (CWW) is superior for evaluating how well websites support users ’ navigation and information search tasks. The CWW uses Latent Semantic Analysis to objectively estimate the degree of semantic similarity (information scent) between representative user goal statements (100-200 words) and heading/link texts on each web page. Using an actual website, the paper shows how the CWW identifies three types of problems in web page designs. Three experiments test CWW predictions of users’ success rates in accomplishing goals, verifying the value of CWW for identifying these usability problems.

The purpose of this paper is to introduce a replicable WWW protocol analysis methodology illustrated by application to data collected in the laboratory. The methodology uses instrumentation to obtain detailed recordings of user actions with a browser, caches Web pages encountered, and videotapes talk-aloud protocols. We apply the current form of the method to the analysis of eight Web protocols, visualizing the structure of the interaction and showing the strong effect of information scent in determining the path followed.

This article describes rational analyses and cognitive models of Web users developed within information foraging theory. This is done by following the rational analysis methodology of (a) characterizing the problems posed by the environment, (b) developing rational analyses of behavioral solutions to those problems, and (c) developing cognitive models that approach the realization of those solutions. Navigation choice is modeled as a random utility model that uses spreading activation mechanisms that link proximal cues (information scent) that occur in Web browsers to internal user goals. Web-site leaving is modeled as an ongoing assessment by the Web user of the expected benefits of continuing at a Web site as opposed to going elsewhere. These cost–benefit assessments are also based on spreading activation models of information scent. Evaluations include a computational model of Web user behavior called Scent-Based Navigation and Information Foraging in the ACT Architecture, and the Law of Surfing, which characterizes the empirical distribution of the length of paths of visitors at a Web site.

Search engine logs not only keep navigation information, but also the queries made by their users. In particular, queries to a search engine follow a power-law distribution, which is far from uniform. Queries and related clicks can be used to improve the search engine itself in different aspects: user interface, index performance, and answer ranking. In this chapter we present some of the main ideas proposed in query mining and we show a few examples based on real data from a search engine focused in the Chilean Web. 1