TY - JOUR
T1 - SNIF-ACT
T2 - A cognitive model of user navigation on the World Wide Web
AU - Fu, Wai Tat
AU - Pirolli, Peter
N1 - Support. Portions of this research have been supported by a start-up fund from the Human Factors Division and Beckman Institute at the University of Illinois as well as from an Office of Naval Research Contract No. N00014-96-C-0097 to P. Pirolli and S.K. Card, and from Advanced Research and Development Activity, Novel Intelligence from Massive Data Program Contract No. MDA904-03-C-0404 to S.K. Card and Peter Pirolli.
PY - 2007
Y1 - 2007
N2 - 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 IFIF 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 SNIFACT 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. SNIFACT 2.0 was validated on a data set obtained from 74 subjects. Monte Carlo simulations of the model showed that SNIFACT 2.0 provided better fits to human data than SNIFACT 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.
AB - 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 IFIF 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 SNIFACT 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. SNIFACT 2.0 was validated on a data set obtained from 74 subjects. Monte Carlo simulations of the model showed that SNIFACT 2.0 provided better fits to human data than SNIFACT 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.
UR - https://www.scopus.com/pages/publications/36749070415
UR - https://www.scopus.com/pages/publications/36749070415#tab=citedBy
M3 - Article
AN - SCOPUS:36749070415
SN - 0737-0024
VL - 22
SP - 355
EP - 412
JO - Human-Computer Interaction
JF - Human-Computer Interaction
IS - 4
ER -