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

By Wai-Tat Fu, Peter Pirolli. Hum. Comput. Interact., 2007

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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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/0d96d03cf822ea1584b468389b3f4bc39164d85f

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Citations in the corpus

Unpacking the Exploration–Exploitation Tradeoff: A Synthesis of Human and Animal Literatures (2015, background): “…et al., 2007), an animal such as a hummingbird or a chipmunk leaving its current patch of food to search elsewhere (Kramer & Weary, to retrieve information about other topics, either from memory (Hills et al., 2012) or from the world, including online (Fu & Pirolli, 2007; Pirolli & Card, 1999).”
Optimal foraging in semantic memory. (2012, background): “…patches are sets of random letters and items are words created from subsets of those letters—Wilke et al., 2009; Hills et al., 2008), and search for information on the web and in other naturalistic environments (where patches can be sets of similar web pages— Pirolli & Card, 1999; Pirolli, 2007).”
How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games (2017, methodology): “IFT constructs have been used to explain and predict people’s information-seeking behavior in several domains, such as understanding navigations through web sites or programming and software engineering environments [5, 7, 8, 14, 19, 22, 23, 24, 29].”
Understanding PubMed® user search behavior through log analysis (2009, background): “Our work presented here provides a valuable background for future investigations of stochastic models [e.g. (54,55)] as well as conceptual models [e.g. (46,56–58)].”