ABSTRACT We describe the development of a computational cognitive model that explains navigation behavior on the World Wide Web. The model, called SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT cognitive architecture), is motivated by Information Foraging Theory (IFT), which quantifies the perceived relevance of a Web link to a user's goal by a spreading activation mechanism. The model assumes that users evaluate links on a Web page sequentially and decide to click on a link or to go back to the previous page by a Bayesian satisficing model (BSM) that adaptively evaluates and selects actions based on a combination of previous and current assessments of the relevance of link texts to information goals. SNIF-ACT 1.0 utilizes the measure of utility, called information scent, derived from IFT to predict rankings of links on different Web pages. The model was tested against a detailed set of protocol data collected from 8 participants as they engaged in two information-seeking tasks using the World Wide Web. The model provided a good match to participants' link selections. In SNIF-ACT 2.0, we included the adaptive link selection mechanism from the BSM that sequentially evaluates links on a Web page. The mechanism allowed the model to dynamically build up the aspiration levels of actions in a satisficing process (e.g., to follow a link or leave a Web site) as it sequential assessed link texts on a Web page. The dynamic mechanism provides an integrated account of how and when users decide to click on a link or leave a page based on the sequential, ongoing experiences with the link context on current and previous Web pages. SNIF-ACT 2.0 was validated on a data set obtained from 74 subjects. Monte Carlo simulations of the model showed that SNIF-ACT 2.0 provided better fits to human data than SNIF-ACT 1.0 and a Position model that used position of links on a Web page to decide which link to select. We conclude that the combination of the IFT and the BSM provides a good description of user-Web interaction. Practical implications of the model are discussed.
%0 Journal Article
%1 fu2007snifact
%A Fu, Wai-Tat
%A Pirolli, Peter L. T.
%D 2007
%J Human-Computer Interaction
%K behavior diss foraging information inthesis navigation scent
%N 4
%P 355-412
%R 10.1080/07370020701638806
%T SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web
%U http://www.tandfonline.com/doi/abs/10.1080/07370020701638806
%V 22
%X ABSTRACT We describe the development of a computational cognitive model that explains navigation behavior on the World Wide Web. The model, called SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT cognitive architecture), is motivated by Information Foraging Theory (IFT), which quantifies the perceived relevance of a Web link to a user's goal by a spreading activation mechanism. The model assumes that users evaluate links on a Web page sequentially and decide to click on a link or to go back to the previous page by a Bayesian satisficing model (BSM) that adaptively evaluates and selects actions based on a combination of previous and current assessments of the relevance of link texts to information goals. SNIF-ACT 1.0 utilizes the measure of utility, called information scent, derived from IFT to predict rankings of links on different Web pages. The model was tested against a detailed set of protocol data collected from 8 participants as they engaged in two information-seeking tasks using the World Wide Web. The model provided a good match to participants' link selections. In SNIF-ACT 2.0, we included the adaptive link selection mechanism from the BSM that sequentially evaluates links on a Web page. The mechanism allowed the model to dynamically build up the aspiration levels of actions in a satisficing process (e.g., to follow a link or leave a Web site) as it sequential assessed link texts on a Web page. The dynamic mechanism provides an integrated account of how and when users decide to click on a link or leave a page based on the sequential, ongoing experiences with the link context on current and previous Web pages. SNIF-ACT 2.0 was validated on a data set obtained from 74 subjects. Monte Carlo simulations of the model showed that SNIF-ACT 2.0 provided better fits to human data than SNIF-ACT 1.0 and a Position model that used position of links on a Web page to decide which link to select. We conclude that the combination of the IFT and the BSM provides a good description of user-Web interaction. Practical implications of the model are discussed.
@article{fu2007snifact,
abstract = { ABSTRACT We describe the development of a computational cognitive model that explains navigation behavior on the World Wide Web. The model, called SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT cognitive architecture), is motivated by Information Foraging Theory (IFT), which quantifies the perceived relevance of a Web link to a user's goal by a spreading activation mechanism. The model assumes that users evaluate links on a Web page sequentially and decide to click on a link or to go back to the previous page by a Bayesian satisficing model (BSM) that adaptively evaluates and selects actions based on a combination of previous and current assessments of the relevance of link texts to information goals. SNIF-ACT 1.0 utilizes the measure of utility, called information scent, derived from IFT to predict rankings of links on different Web pages. The model was tested against a detailed set of protocol data collected from 8 participants as they engaged in two information-seeking tasks using the World Wide Web. The model provided a good match to participants' link selections. In SNIF-ACT 2.0, we included the adaptive link selection mechanism from the BSM that sequentially evaluates links on a Web page. The mechanism allowed the model to dynamically build up the aspiration levels of actions in a satisficing process (e.g., to follow a link or leave a Web site) as it sequential assessed link texts on a Web page. The dynamic mechanism provides an integrated account of how and when users decide to click on a link or leave a page based on the sequential, ongoing experiences with the link context on current and previous Web pages. SNIF-ACT 2.0 was validated on a data set obtained from 74 subjects. Monte Carlo simulations of the model showed that SNIF-ACT 2.0 provided better fits to human data than SNIF-ACT 1.0 and a Position model that used position of links on a Web page to decide which link to select. We conclude that the combination of the IFT and the BSM provides a good description of user-Web interaction. Practical implications of the model are discussed. },
added-at = {2017-01-27T19:01:59.000+0100},
author = {Fu, Wai-Tat and Pirolli, Peter L. T.},
biburl = {https://www.bibsonomy.org/bibtex/2494613b5b6e2af38465c53602992f52b/becker},
doi = {10.1080/07370020701638806},
eprint = {http://www.tandfonline.com/doi/pdf/10.1080/07370020701638806},
interhash = {e99a2d259bb63b5a5062a23291f4bff9},
intrahash = {494613b5b6e2af38465c53602992f52b},
journal = {Human-Computer Interaction},
keywords = {behavior diss foraging information inthesis navigation scent},
number = 4,
pages = {355-412},
timestamp = {2017-12-19T09:23:48.000+0100},
title = {SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web},
url = {http://www.tandfonline.com/doi/abs/10.1080/07370020701638806},
volume = 22,
year = 2007
}