CoLiDeS þ Pic is a cognitive model of web-navigation that incorporates semantic information from pictures into CoLiDeS. In our earlier research, we have demonstrated that by incorporating semantic information from pictures, CoLiDeS þ Pic can predict the hyperlinks on the shortest path more frequently, and also with greater information scent, compared to earlier cognitive models of web-navigation like CoLiDeS that relied only on textual information from hyperlinks. In this article, we investigate the following research questions. First, would the increase in information scent have an impact on the actual user navigation behaviour? Second, do users actually follow the navigation path predicted by CoLiDeS þ Pic? In other words, would CoLiDeS þ Pic predict actual user navigation behaviour more accurately than CoLiDeS? We investigate these questions by varying the relevance of pictures on a web page and studying the impact of varying relevance on the user navigation patterns. We found that under the highly relevant picture condition, users were more accurate and took less time to finish their tasks. Also, under the highly relevant picture condition, CoLiDeS þ Pic predicts significantly greater number of actual user clicks. There was no significant difference in model predictions between the lowly relevant picture condition and nopicture condition. These results validate the predictions made by CoLiDeS þ Pic.
Computational cognitive models developed so far do not incorporate individual differences in domain knowledge in predicting user clicks on search result pages. We address this problem using a cognitive model of information search which enables us to use two semantic spaces having a low (non-expert semantic space) and a high (expert semantic space) amount of medical and health related information to represent respectively low and high knowledge of users in this domain. We also investigated two different processes along which one can gain a larger amount of knowledge in a domain: an evolutionary and a common core process. Simulations of model click behavior on difficult information search tasks and subsequent matching with actual behavioral data from users (divided into low and high domain knowledge groups based on a domain knowledge test) were conducted. Results showed that the efficacy of modeling for high domain knowledge participants (in terms of the number of matches between the model predictions and the actual user clicks on search result pages) was higher with the expert semantic space compared to the nonexpert semantic space while for low domain knowledge participants it was the other way around. When the process of knowledge acquisition was taken into account, the effect of using a semantic space based on high domain knowledge was significant only for high domain knowledge participants, irrespective of the knowledge acquisition process. The implications of these outcomes for support tools that can be built based on these models are discussed.
This paper looks at two limitations of cognitive models of webnavigation: first, they do not account for the entire process of information search and second, they do not account for the differences in search behavior caused by aging. To address these limitations, data from an experiment in which two types of information search tasks (simple and difficult), presented to both young and old participants was used. We found that in general difficult tasks demand significantly more time, significantly more clicks, significantly more reformulations and are answered significantly less accurately than simple tasks. Older persons inspect the search engine result pages significantly longer, produce significantly fewer reformulations with difficult tasks than younger persons, and are significantly more accurate than younger persons with simple tasks. We next used a cognitive model of web-navigation called CoLiDeS to predict which search engine result a user would choose to click. Old participants were found to click more often only on search engine results with high semantic similarity with the query. Search engine results generated by old participants were of higher semantic similarity value (computed w.r.t the query) than those generated by young participants only in the second cycle. Match between modelpredicted clicks and actual user clicks was found to be significantly higher for difficult tasks compared to simple tasks. Potential improvements in enhancing the modeling and its applications are discussed.
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.
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