Abstract
We describe VUDM, our Visual User-model Data Mining tool, and its application to data logged regarding interactions of 1,200 users of the Networked Digital Library of Theses and Dissertations (NDLTD). The goals of VUDM are to visualize social networks, patrons’ distributions, and usage trends of NDLTD. The distinctive approach of this research is that we focus on analysis and visualization of users’ implicit rating data, which was generated based on user tracking information, such as sending queries and browsing result sets – rather than focusing on explicit data obtained from a user survey, such as major, specialties, years of experience, and demographics. The VUDM interface uses spirals to portray virtual interest groups, positioned based on inter-group relationships. VUDM facilitates identifying trends related to changes in interest, as well as concept drift. A formative evaluation found that VUDM is perceived to be effective for five types of tasks. Future work will aim to improve the understandability and utility of VUDM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kumar, R., Novak, J., Raghavan, P., Tomkins, A.: Structure and Evolution of Blogspace. Communications of the ACM 47(12), 35–39 (2004)
Soukup, T., Davidson, I.: Visual Data Mining: Techniques and Tools for Data Visualization and Mining. Wiley Computer Publishing, Chichester (2002)
Keim, D.A.: Information Visualization and Visual Data Mining. IEEE Transaction on Visualization and Computer Graphics 8(1), 1–8 (2002)
Xu, J., Chen, H.: Criminal Network Analysis and Visualization. Communications of the ACM 48(6), 101–107 (2005)
Boyd, D., Potter, J.: Social Network Fragments: An Interactive Tool for Exploring Digital Social Connections. In: Proceedings of International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 2003), p. 1 (2003)
Heer, J., Boyd, D.: Vizster: Visualizing Online Social Networks. In: Proceeding of the 2005 IEEE Symposium on Information Visualization (INFOVIS 2005), p. 5 (2005)
Wise, J.A., Thomas, J.J., Pen-nock, K., Lantrip, D., Pottier, M., Schur, A.: Visualizing the non-visual: Spatial analysis and interaction with information from text documents. In: Proc. of the Information Visualization Symposium, pp. 51–58. IEEE Computer Society Press, Los Alamitos (1995)
Manavoglu, E., Pavlov, D., Giles, C.L.: Probabilistic User Behavior Models. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM 2003), pp. 203–210 (2003)
Pazzani, M., Billsus, D.: Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27, 313–331 (1997)
Tang, T.Y., McCalla, G.: Mining Implicit Ratings for Focused Collaborative Filtering for Paper Recommendations. In: Proceedings of Workshop on User and Group models for Web-based Adaptive Collaborative Environments (UM 2003) (2006), available at http://www.ia.uned.es/~elena/um03-ws/
Webb, G.I., Pazzani, M.J., Billsus, D.: Machine Learning for User Modeling, User Modeling and User-Adapted Interaction, vol. 11, pp. 19–29. Kluwer Academic Publisher, Dordrecht (2001)
NDLTD, Networked Digital Library of Theses and Dissertations (2006), available at http://www.ndltd.org
Murthy, U., Vasile, S., Ahuja, K.: Virginia Tech CS class project report (2006), available at http://collab.dlib.vt.edu/runwiki/wiki.pl?IsRproj_UserMod_Con
Kim, S., Murthy, U., Ahuja, K., Vasile, S., Fox, E.A.: Effectiveness of Implicit Rating Data on Characterizing Users in Complex Information Systems. In: Rauber, A., Christodoulakis, S., Tjoa, A.M. (eds.) ECDL 2005. LNCS, vol. 3652, pp. 186–194. Springer, Heidelberg (2005)
Herman, I., Melançon, G., Marshall, M.S.: Graph Visualization and Navigation in Information Visualization: A Survey. IEEE Transactions on Visualization and Computer Graphics 6(1), 24–43 (2000)
Kim, S., Fox, E.A.: Interest-based User Grouping Model for Collaborative Filtering in Digital Libraries. In: Chen, Z., Chen, H., Miao, Q., Fu, Y., Fox, E., Lim, E.-p. (eds.) ICADL 2004. LNCS, vol. 3334, pp. 533–542. Springer, Heidelberg (2004)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. A Wiley-Interscience Publication, Chichester (2000)
Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23, 69–101 (1996)
Hix, D., Hartson, H.R.: Developing User Interfaces: Ensuring Usability Through Product & Process. Wiley Professional Computing, Chichester (1993)
CITIDEL, Computing and Information Technology Interactive Digital Educational Library (2006), available at http://www.citidel.org
ETANA-DL, Managing complex information applications: An archaeology digital library (2006), available at http://etana.dlib.vt.edu
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, S., Lele, S., Ramalingam, S., Fox, E.A. (2006). Visualizing User Communities and Usage Trends of Digital Libraries Based on User Tracking Information. In: Sugimoto, S., Hunter, J., Rauber, A., Morishima, A. (eds) Digital Libraries: Achievements, Challenges and Opportunities. ICADL 2006. Lecture Notes in Computer Science, vol 4312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11931584_14
Download citation
DOI: https://doi.org/10.1007/11931584_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49375-4
Online ISBN: 978-3-540-49377-8
eBook Packages: Computer ScienceComputer Science (R0)