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SNIF-ACT : A Cognitive Model of User Navigation on the World Wide Web

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Contents

Introduction

Theory

SNIF-ACT

SNIF-ACT 1.0

SNIF-ACT 2.0

General Discussion

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■ A substantial number of people now turn to the World Wide Web in search of such knowl-edge. Consequently, the Web has become a domain that allows the study of complex ev-eryday human cognition.

■ The purpose of this article is to present a computational cognitive model that simulates how people seek information on the Web.

■ This model is called SNIF-ACT, which stands for Scent-based Navigation and Informa-tion Foraging in the ACT architecture.

■ This article reports on two versions of SNIF-ACT (versions 1.0 and 2.0) that have been developed to model how users navigate through the Web in search of answers to specific information-seeking tasks.

Introduction

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■ It assumes that a user has the goal of finding information about “medical treatments for cancer” and encounters a Web link labeled with the text that includes cell, patient, dose, and beam.

Theory _Information Foraging

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Theory _Information Foraging (continued)

■ Activation Equation

■ Information Scent Equation (Utility Calculation)

■ Attentional Weight Equation

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■ Similar to ACT–R models, SNIF-ACT has two memory components: the declarative memory component and the procedural memory component.

SNIF-ACT _Declarative Knowledge & Procedural Knowledge

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SNIF-ACT _Selection of Actions

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■ Antz Task

SNIF-ACT 1.0 _Tasks and Users

After installing a state of the art entertainment center in your den and replacing the furniture and carpeting, your redecorating is al-most complete. All that remains to be done is to purchase a set of movie posters to hang on the walls. Find a site where you can pur-chase the set of four Antz movie posters depicting the princess, the hero, the best friend, and the general.

■ City Task

You are the Chair of Comedic events for Louisiana State University in Baton Rouge, LA. Your computer has just crashed and you have lost several advertisements for upcoming events. You know that The Second City tour is coming to your theater in the spring, but you do not know the precise date. Find the date the comedy troupe is playing on your campus. Also find a photograph of the group to put on the advertisement.

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SNIF-ACT 1.0 _Tasks and Users (Continued)

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■ The utility of these Click-Link instantiations is calculated using the information scent equation previously presented. The probability that a particular Click-Link production is selected and executed is calculated using a kind of RUM (The random utility model of link choice).

SNIF-ACT 1.0 _Utility Calculations

■ Conflict resolution equation

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SNIF-ACT 1.0 _Results

Link Selections

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SNIF-ACT 1.0 _Results

Site-Leaving Actions

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■ We also found that the simple information of link position on a Web page also seems to predict link selections.

■ The results are consistent with the idea that the link selection process involves a dynamic evaluation process that operates on both information scent and the position or sequential order of links.

■ In SNIF-ACT 2.0, we hypothesize that during the link selection process, current and pre-vious experiences with different link texts and Web sites interact dynamically and influ-ence the final selection.

SNIF-ACT 2.0

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■ To evaluate SNIF-ACT 2.0, we expanded our data sets to include more participants and more tasks (Chi et al., 2003).

■ Both the Yahoo and Parc Web sites had been tested with a set of eight tasks, for a total of 8 × 2 = 16 tasks.

SNIF-ACT 2.0 _Tasks and Users

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SNIF-ACT 2.0 _Utility Calculations

■ Attend to Link

■ Click Link

■ Backup a Page

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SNIF-ACT 2.0 _Utility Calculations

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SNIF-ACT 2.0 _Results

Link Selections

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SNIF-ACT 2.0 _Results

Going back to previous page

We then performed the same regression analyses as we did when we tested SNIF-ACT 2.0 predictions on link selection. We obtained R2 = .73 and .80 or the ParcWeb and Yahoo sites, respectively.

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SNIF-ACT 2.0 _Results

Success in finding the target pages

Easy Easy

HardHardHard

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■ This is important, as it demonstrates the model’s capability to predict task difficulties and how it can be extended to an automatic usability analyses tool, which we describe in the Discussion section next.

■ Although the model is tested only on interactions between humans and the WWW, we be-lieve that the fundamental principles behind the model are general enough to be applica-ble to other large information structures.

■ An analysis is then done by Bloodhound and a report is then automatically generated that shows such measures as the predicted number of users who will be able to find target in-formation relevant to the specified task, as well as intermediate navigation pages that are predicted to be highly visited that may be a cause of bottlenecks.

■ The development of an automatic tool that accurately models user-Web behavior will greatly facilitate the interactive process of developing and evaluating Web sites.

General Discussion

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General Discussion (continued)

■ MESA

MESA (Miller & Remington, 2004) is intended to be a cognitive en-gineering model for calculating the time cost of navigation through alternative Web structures for given tasks. MESA does not have an automated way of computing link relevance (the information scent of links), requiring that modelers separately obtain ratings of stated references for links. Both of these concerns are addressed by the SNIF-ACT model.

■ CoLiDes

CoLiDes (Kitajima et al., 2005) is model of Web navigation that derivesfrom Kintsch’s (1998) construction-integration cognitive architec-ture. This includes prediction of the total number of clicks to ac-complish a goal (a measure of task difficulty), the identification of problems due to lack of familiar wording on Web pages, links that compete for attention, and links that have weak information scent.