Integrating Domain Knowledge Differences into Modeling User Clicks on Search Result Pages

Abstract

Computational cognitive models developed so far do not incorporate any effect of individual differences in domain knowledge of users 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 low (general semantic space) and high (special semantic space) amount of medical and health related information to represent respectively the low and high knowledge of users in this domain. Simulations on six difficult information search tasks and subsequent matching with actual behavioural data from 48 users (divided into low and high domain knowledge groups based on a domain knowledge test) were conducted. Results showed that the efficacy of modeling user selections on search results (in terms of the number of matches between users and the model and the mean semantic similarity values of the matched search results) is higher with the special semantic space compared to the general semantic space for high domain knowledge participants while for low domain knowledge participants it is the other way around. Implications for support tools that can be built based on these models are discussed.