Abstract
SNIP-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). SNIP-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 SNIP-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. SNIP-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 SNIP-ACT model is able to generate useful predictions about complex user-WWW interactions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 45-54 |
| Number of pages | 10 |
| Journal | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| Volume | 2702 |
| DOIs | |
| State | Published - 2003 |
| Externally published | Yes |
| Event | 9th International Conference, UM 2003 - Johnstown, PA, United States Duration: Jun 22 2003 → Jun 26 2003 |
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science