%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 }