SNIF-ACT: A Model of Information Foraging on the World Wide Web
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.
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- 2.Anderson, J.R. and C. Lebiere, The atomic components of thought. 2000, Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
- 3.Pirolli, P., et al. A user-tracing architecture for modeling interaction with the World Wide Web. in Advanced Visual Interfaces, AVI 2002. 2002. Trento, Italy: ACM Press.Google Scholar
- 4.Pirolli, P., Cognitive engineering models and cognitive architectures in human-computer interaction, in Handbook of applied cognition, F.T. Durso, et al., Editors. 1999, John Wiley & Sons: West Sussex, England. p. 441–477.Google Scholar
- 5.Lynch, G., S. Palmiter, and C. Tilt. The Max model: A standard web site user model. in Human Factors and the Web. 1999.Google Scholar
- 6.Card, S.K., T.P. Moran, and A. Newell, The psychology of human-computer interaction. 1983, Hillsdale, New Jersey: Lawrence Erlbaum Associates.Google Scholar
- 7.Pirolli, P., A web site user model should at least predict something about users. internetworking, 2000. 3.Google Scholar
- 8.Byrne, M.D., et al. The tangled web we wove: A taxonomy of WWW use. in Human Factors in Computing Systems, CHI’ 99. 1999. Pittsburgh, PA: ACM Press.Google Scholar
- 9.Blackmon, M.H., et al. Cognitive Walkthrough for the Web. in Human Factors in Computing Systems, CHI 2002. 2002. Minneapolis, MN: ACM Press.Google Scholar
- 10.Pirolli, P. Computational models of information scent-following in a very large browsable text collection. in Conference on Human Factors in Computing Systems, CHI’ 97. 1997. Atlanta, GA: Association for Computing Machinery.Google Scholar
- 11.Harman, D. Overview of the first text retrieval conference. in 16th Annual International ACM/SIGIR Conference. 1993. Pittsburgh, PA: ACM.Google Scholar
- 12.Morrison, J.B., P. Pirolli, and S.K. Card. A taxonomic analysis of what World Wide Web activities significantly impact people’s decisions and actions. in Conference on Human Factors in Computing Systems, CHI’ 01. 2001. Seattle, WA: ACM Press.Google Scholar
- 13.Ericsson, K.A. and H.A. Simon, Protocol Analysis: Verbal reports as data. 1984, Cambridge, MA: MIT Press.Google Scholar
- 14.Chi, E.H., et al. The Bloodhound Project: Automating discovery of Web usability issues using the InfoScent simulator. in Conference on Human Factors in Computing Systems, CHI 2003. 2003. Fort Lauderdale, FL: ACM Press.Google Scholar