Browse > Article
http://dx.doi.org/10.1633/JISTaP.2020.8.3.3

A Conceptual Framework for an Information Behavior Model Based on the Collaboration Perspective between User and System for Information Retrieval  

Yangyuen, Wachira (Management of Information Technology Program, School of Informatics, Walailak University)
Phetkaew, Thimaporn (School of Informatics, Walailak University)
Nuntapichai, Siwanath (School of Informatics, Walailak University)
Publication Information
Journal of Information Science Theory and Practice / v.8, no.3, 2020 , pp. 30-46 More about this Journal
Abstract
This research aimed (1) to study and analyze the ability of current information retrieval (IR) systems based on views of information behavior (IB), and (2) to propose a conceptual framework for an IB model based on the collaboration between the system and user, with the intent of developing an IR system that can apply intelligent techniques to enhance system efficiency. The methods in this study consisted of (1) document analysis which included studying the characteristics and efficiencies of the current IR systems and studying the IB models in the digital environment, and (2) implementation of the Delphi technique through an indepth interview method with experts. The research results were presented in three main parts. First, the IB model was categorized into eight stages, different from traditional IB, in the digital environment, which can correspond to all behaviors and be applied to with an IR system. Second, insufficient functions and log file storage hinder the system from effectively understanding and accommodating user behavior in the digital environment. Last, the proposed conceptual framework illustrated that there are stages that can add intelligent techniques to the IR system based on the collaboration perspective between the user and system to boost the users' cognitive ability and make the IR system more user-friendly. Importantly, the conceptual framework for the IB model based on the collaboration perspective between the user and system for IR assisted the ability of information systems to learn, recognize, and comprehend human IB according to individual characteristics, leading to enhancement of interaction between the system and users.
Keywords
information behavior; information behavior model; collaborative information behavior model; information retrieval;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Astakhova, L. V. (2018). The information behavior of users of digital resources as an object of technological monitoring in the knowledge-based society. Scientific and Technical Information Processing, 45(4), 209-218.   DOI
2 Buettner, R. (2017). Predicting user behavior in electronic markets based on personality-mining in large online social networks. Electronic Markets, 27(3), 247-265.   DOI
3 Choo, C. W., Detlor, B., & Turnbull, D. (2000). Information seeking on the web: An integrated model of browsing and searching. First Monday, 5(2).
4 Dresang, E. T. (2005). The information-seeking behavior of youth in the digital environment. Library Trends, 54(2), 178-196.   DOI
5 Ellis, D., Cox, D., & Hall, K. (1993). A comparison of the information seeking patterns of researchers in the physical and social sciences. Journal of Documentation, 49(4), 356-369.   DOI
6 Gooding, P. (2016). Exploring the information behaviour of users of Welsh Newspapers Online through web log analysis. Journal of Documentation, 72(2), 232-246.   DOI
7 Jiang, J., He, D., & Allan, J. (2014, July 6-11). Searching, browsing, and clicking in a search session: Changes in user behavior by task and over time. Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 607-616). ACM.
8 Hofmann, K., Li, L., & Radlinski, F. (2016). Online evaluation for information retrieval. Foundations and $Trends^{(R)}$ in Information Retrieval, 10(1), 1-117.   DOI
9 Imenda, S. (2014). Is there a conceptual difference between theoretical and conceptual frameworks? Journal of Social Sciences, 38(2), 185-195.   DOI
10 Jiang, D., Pei, J., & Li, H. (2013). Mining search and browse logs for web search: A survey. ACM Transactions on Intelligent Systems and Technology, 4(4), 57.
11 Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4-5), 441-504.   DOI
12 Kuhlthau, C. C. (1991). Inside the search process: Information seeking from the user's perspective. Journal of the American Society for Information Science, 42(5), 361-371.   DOI
13 Maleki-Dizaji, S., Siddiqi, J., Soltan-Zadeh, Y., & Rahman, F. (2014). Adaptive information retrieval system via modelling user behaviour. Journal of Ambient Intelligence and Humanized Computing, 5(1), 105-110.   DOI
14 Spink, A., & Cole, C. (2001). Introduction to the special issue: Everyday life information-seeking research. Library & Information Science Research, 23(4), 301-304.   DOI
15 Marchionini, G. (1995). Information seeking in electronic environments. New York: Cambridge University Press.
16 Meho, L. I., & Tibbo, H. R. (2003). Modeling the informationseeking behavior of social scientists: Ellis's study revisited. Journal of the American Society for Information Science and Technology, 54(6), 570-587.   DOI
17 Petrovskiy, M. (2006, September 11-14). A data mining approach to learning probabilistic user behavior models from database access log. In J. Filipe, B. Shishkov, & M. Helfert (Eds.), ICSOFT 2006: International Conference on Software and Data Technologies (pp. 323-332). Springer.
18 Saracevic, T. (1996, October 21-24). Modeling interaction in information retrieval (IR): A review and proposal. Proceedings of the American Society for Information Science (pp. 3-9). ASIS.
19 Speretta, M., & Gauch, S. (2005, September 19-22). Personalized search based on user search histories. Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (pp. 622-628). IEEE Computer Society.
20 Spink, A., & Wilson, T. D. (1999, April 14-16). Toward a theoretical framework for information retrieval (IR) evaluation in an information seeking context. Proceedings of the 1999 international conference on Final Mira (pp. 1-9). BCS Learning and Development Ltd.
21 Yuan, X., & J. Belkin, N. J. (2014). Applying an informationseeking dialogue model in an interactive information retrieval system. Journal of Documentation, 70(5), 829-855.   DOI
22 Sugiyama, K., Hatano, K., & Yoshikawa, M. (2004, May 17-20). Adaptive web search based on user profile constructed without any effort from users. Proceedings of the 13th International Conference on World Wide Web (pp. 675-684). ACM.
23 Wilson, T. D. (2000). Human information behavior. Informing Science, 3(2), 49-56.   DOI
24 Yangyuen, Y., Nuntapichai, S., & Phetkaew, T. (2016). Information behavior model: Perspective for era digital environment. Journal of Information Science and Technology, 6(1), 34-44.