Performance of computational cognitive models of web-navigation on real websites

Computational cognitive models of web-navigation developed so far have largely been tested only on mock-up websites. In this paper, for the first time, we compare and contrast the performance of two models, CoLiDeS and CoLiDeS+, on two real websites from the domains of technology and health, under two conditions of task difficulty, simple and difficult. We found that CoLiDeS+ predicted more hyperlinks on the correct path and had a higher path completion ratio than CoLiDeS. CoLiDeS+ found the target page more often than CoLiDeS, took more steps to reach the target page and was more 'disoriented' than CoLiDeS for difficult tasks. Difficult tasks in general for both models had less task success and lower path completion ratio, predicted less hyperlinks on the correct path, visited pages with lower mean LSA and took more steps to complete compared with simple tasks. Overall, inclusion of context from previously visited pages and implementation of backtracking strategies (which are both part of CoLiDeS+) led to better modelling performance. Suggestions to further improve the performance of these computational cognitive models on real websites are discussed.