Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Understanding and Improving Information SearchHow Cognitive Computational Models Can Improve Information Search

Understanding and Improving Information Search: How Cognitive Computational Models Can Improve... [This chapter discusses why and how a computational cognitive model that captures the broader set of processes of information search is important and useful. The first reason why this can be useful is when information search is only one component of a broader task. A better understanding of the broader process of information search can lead to better metrics of relevance that are specific to the broader task in which the user is engaged. The second reason is that it helps to develop better personalized tools that are more compatible with the individual users as they search information for different purposes. Two examples of such computational cognitive models are presented. The first model, SNIF-ACT, demonstrates the value of adopting a theory-based mechanism, called the Bayesian satisficing mechanism (BSM), that selects information search strategies based on ongoing assessment of the information scent cues encountered by a user as he or she navigates across Web pages. The second model, ESL, tracks both learning of knowledge structures and search behavior in a social tagging system over a period of eight weeks as they continuously search for Web documents. These computational cognitive models generate explicit predictions on what users will do when they interact with different information retrieval systems for different tasks in different contexts. Computational cognitive models therefore complement existing computational techniques that aim to improve information or document retrieval. At the same time, they allow researchers to develop and test unified theories of information search by integrating the vast literature on information search behavior in different contexts.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Understanding and Improving Information SearchHow Cognitive Computational Models Can Improve Information Search

Part of the Human–Computer Interaction Series Book Series
Editors: Fu, Wai Tat; van Oostendorp, Herre
Fu, Wai Tat
Understanding and Improving Information Search — May 30, 2020
Download PDF
16 pages

Loading next page...
 
/lp/springer-journals/understanding-and-improving-information-search-how-cognitive-ZoH75mlt00
Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2020
ISBN
978-3-030-38824-9
Pages
29 –45
DOI
10.1007/978-3-030-38825-6_3
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter discusses why and how a computational cognitive model that captures the broader set of processes of information search is important and useful. The first reason why this can be useful is when information search is only one component of a broader task. A better understanding of the broader process of information search can lead to better metrics of relevance that are specific to the broader task in which the user is engaged. The second reason is that it helps to develop better personalized tools that are more compatible with the individual users as they search information for different purposes. Two examples of such computational cognitive models are presented. The first model, SNIF-ACT, demonstrates the value of adopting a theory-based mechanism, called the Bayesian satisficing mechanism (BSM), that selects information search strategies based on ongoing assessment of the information scent cues encountered by a user as he or she navigates across Web pages. The second model, ESL, tracks both learning of knowledge structures and search behavior in a social tagging system over a period of eight weeks as they continuously search for Web documents. These computational cognitive models generate explicit predictions on what users will do when they interact with different information retrieval systems for different tasks in different contexts. Computational cognitive models therefore complement existing computational techniques that aim to improve information or document retrieval. At the same time, they allow researchers to develop and test unified theories of information search by integrating the vast literature on information search behavior in different contexts.]

Published: May 30, 2020

There are no references for this article.