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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Second International Workshop on Search as Learning, co-located with the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016) |
| Editors | Jacek Gwizdka, Preben Hansen, Claudia Hauff, Jiyin He, Noriko Kando |
| Publisher | CEUR WS |
| Number of pages | 5 |
| Volume | 1647 |
| Publication status | Published - 21 Jul 2016 |
Keywords
- Modeling
- Information Search
- Cognitive Factors
- Prior Domain Knowledge