ABSTRACT
A number of cognitive processes are involved in the process of information search on the Internet: memory, attention, comprehension, problem solving, executive control and decision making. Several cognitive factors such as aging-related cognitive abilities, domain knowledge, spatial ability and need for cognition, etc. in turn influence either positively or negatively these cognitive processes. Traditional click models from information retrieval community that predict user clicks do not fully take into account the effect of the above cognitive factors. We propose to exploit the capabilities of computational cognitive models to simulate the effects of cognitive factors on information search behavior. In this direction, we present some ideas how to incorporate these factors into a computational cognitive model called CoLiDeS+. Preliminary analysis of our ideas on modeling and predicting individual differences in information search due to age and domain knowledge show promising outcomes.
- Marilyn Hughes Blackmon, Muneo Kitajima, and Peter G Polson. 2005. Tool for accurately predicting website navigation problems, non-problems, problem severity, and effectiveness of repairs. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 31--40. Google ScholarDigital Library
- Marilyn Hughes Blackmon, Dipti R Mandalia, Peter G Polson, and Muneo Kitajima. 2007. Automating usability evaluation: Cognitive walkthrough for the web puts LSA to work on real-world HCI design problems. Handbook of latent semantic analysis (2007), 345--375.Google Scholar
- Pia Borlund and Peter Ingwersen. 1997. The development of a method for the evaluation of interactive information retrieval systems. Journal of Documentation 53, 3 (1997), 225--250.Google ScholarCross Ref
- Raluca Budiu and John R Anderson. 2004. Interpretation-based processing: A unified theory of semantic sentence comprehension. Cognitive Science 28, 1 (2004), 1--44.Google ScholarCross Ref
- Aline Chevalier, Aurélie Dommes, and Jean-Claude Marquié. 2015. Strategy and accuracy during information search on the Web: Effects of age and complexity of the search questions. Computers in Human Behavior 53 (2015), 305--315. Google ScholarDigital Library
- Jessie Chin, Evan Anderson, Chieh-Li Chin, and Wai-Tat Fu. 2015. Age differences in information search: An exploration-exploitation tradeoff model. In Proceedings of the Human Factors and Ergonomic Society (HFES 2015). 85--89.Google ScholarCross Ref
- Aleksandr Chuklin, Ilya Markov, and Maarten de Rijke. 2015. Click Models for Web Search. Synthesis Lectures on Information Concepts, Retrieval, and Services 7, 3 (2015), 1--115.Google ScholarCross Ref
- Andy Cockburn and Bruce McKenzie. 2001. What do web users do? An empirical analysis of web use. International Journal of Human-Computer Studies 54, 6 (2001), 903--922. Google ScholarDigital Library
- Arthur R Cohen, Ezra Stotland, and Donald M Wolfe. 1955. An experimental investigation of need for cognition. The Journal of Abnormal and Social Psychology 51, 2 (1955), 291--294.Google ScholarCross Ref
- Michael J Cole, Xiangmin Zhang, Chang Liu, Nicholas J Belkin, and Jacek Gwizdka. 2011. Knowledge effects on document selection in search results pages. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1219--1220. Google ScholarDigital Library
- Aurelie Dommes, Aline Chevalier, and Sarah Lia. 2011. The role of cognitive flexibility and vocabulary abilities of younger and older users in searching for information on the web. Applied Cognitive Psychology 25, 5 (2011), 717--726.Google ScholarCross Ref
- Shuk Ying Ho. 2005. An exploratory study of using a user remote tracker to examine web users' personality traits. In Proceedings of the 7th International Conference on Electronic Commerce. ACM, 659--665. Google ScholarDigital Library
- John L Horn. 2012. The Theory of Fluid and Crystallized Intelligence in Relation to Concepts of Cognitive Psychology and Aging in. In Aging and Cognitive Processes. Vol. 8. Springer Science & Business Media, 237--278.Google Scholar
- Botao Hu, Yuchen Zhang, Weizhu Chen, Gang Wang, and Qiang Yang. 2011. Characterizing search intent diversity into click models. In Proceedings of the 20th International Conference on World Wide Web. ACM, 17--26. Google ScholarDigital Library
- Ion Juvina and Herre van Oostendorp. 2008. Modeling semantic and structural knowledge in Web navigation. Discourse Processes 45, 4-5 (2008), 346--364.Google ScholarCross Ref
- Saraschandra Karanam, Herre van Oostendorp, Mylène Sanchiz, Aline Chevalier, Jessie Chin, and Wai Tat Fu. 2015. Modeling and predicting information search behavior. In Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics. ACM, 7. Google ScholarDigital Library
- Walter Kintsch. 1998. Comprehension: A paradigm for cognition. Cambridge University Press.Google Scholar
- Muneo Kitajima, Marilyn H Blackmon, and Peter G Polson. 2000. A comprehension-based model of Web navigation and its application to Web usability analysis. People and Computers (2000), 357--374.Google Scholar
- Muneo Kitajima, Marilyn Hughes Blackmon, and Peter G Polson. 2005. Cognitive architecture for website design and usability evaluation: Comprehension and information scent in performing by exploration. In HCI International 2005, Vol. 4. L. Erlbaum Associates Mahwah, NJ.Google Scholar
- Thomas K Landauer, Danielle S McNamara, Simon Dennis, and Walter Kintsch. 2007. Handbook of latent semantic analysis. Mahwah,NJ: Erlbaum.Google Scholar
- Jingjing Liu and Xiangmin Zhang. 2008. The effect of need for cognition on search performance. Proceedings of the American Society for Information Science and Technology 45, 1 (2008), 1--12.Google ScholarCross Ref
- Heidar Mokhtari, Mohammad-Reza Davarpanah, Mohammad-Hossein Dayyani, and Mohammad-Reza Ahanchian. 2013. Students' need for cognition affects their information seeking behavior. New Library World 114, 11/12 (2013), 542--549.Google ScholarCross Ref
- Sophie Monchaux, Franck Amadieu, Aline Chevalier, and Claudette Mariné. 2015. Query strategies during information searching: Effects of prior domain knowledge and complexity of the information problems to be solved. Information Processing & Management 51, 5 (2015), 557--569. Google ScholarDigital Library
- Peter Pirolli and Stuart Card. 1999. Information foraging. Psychological Review 106, 4 (1999), 643.Google ScholarCross Ref
- Tara L Queen, Thomas M Hess, Gilda E Ennis, Keith Dowd, and Daniel Grühn. 2012. Information search and decision making: Effects of age and complexity on strategy use. Psychology and aging 27, 4 (2012), 817.Google Scholar
- Joseph Sharit, Mario A Hernández, Sara J Czaja, and Peter Pirolli. 2008. Investigating the roles of knowledge and cognitive abilities in older adult information seeking on the web. ACM Transactions on Computer-Human Interaction (TOCHI) 15, 1 (2008), 3. Google ScholarDigital Library
- Si Shen, Botao Hu, Weizhu Chen, and Qiang Yang. 2012. Personalized click model through collaborative filtering. In Proceedings of the fifth ACM International Conference on Web Search and Data Mining. ACM, 323--332. Google ScholarDigital Library
- Franklin P Tamborello II and Michael D Byrne. 2005. Information search: the intersection of visual and semantic space. In CHI'05 Extended Abstracts on Human Factors in Computing Systems. ACM, 1821--1824. Google ScholarDigital Library
- Jing-Jen Wang and Alan S Kaufman. 1993. Changes in fluid and crystallized intelligence across the 20-to 90-year age range on the K-BIT. Journal of Psychoeducational Assessment 11, 1 (1993), 29--37.Google ScholarCross Ref
- Qianli Xing, Yiqun Liu, Jian-Yun Nie, Min Zhang, Shaoping Ma, and Kuo Zhang. 2013. Incorporating user preferences into click models. In Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management. ACM, 1301--1310. Google ScholarDigital Library
Index Terms
- Modeling individual differences in information search
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