Visualizing User Communities and Usage Trends of Digital Libraries Based on User Tracking Information

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.

[1]  A KeimDaniel.  Information Visualization and Visual Data Mining , 2002 .

[2]  H. Rex Hartson,et al.  Developing user interfaces: ensuring usability through product & process , 1993 .

[3]  Geoffrey I. Webb,et al.  # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Machine Learning for User Modeling , 1999 .

[4]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[5]  David G. Stork,et al.  Pattern Classification , 1973 .

[6]  Gerhard Widmer,et al.  Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.

[7]  Edward A. Fox,et al.  Effectiveness of Implicit Rating Data on Characterizing Users in Complex Information Systems , 2005, ECDL.

[8]  Zhaoneng Chen.  Digital Libraries: International Collaboration and Cross-Fertilization, 7th International Conference on Asian Digital Libraries, ICADL 2004, Shanghai, China, December 13-17, 2004, Proceedings , 2004, ICADL.

[9]  C. Lee Giles,et al.  Probabilistic user behavior models , 2003, Third IEEE International Conference on Data Mining.

[10]  Oliver Günther,et al.  Privacy in e-commerce: stated preferences vs. actual behavior , 2005, CACM.

[11]  Ivan Herman,et al.  Graph Visualization and Navigation in Information Visualization: A Survey , 2000, IEEE Trans. Vis. Comput. Graph..

[12]  Ian Davidson,et al.  Visual Data Mining: Techniques and Tools for Data Visualization and Mining , 2002 .

[13]  Ravi Kumar,et al.  Structure and evolution of blogspace , 2004, CACM.

[14]  Hsinchun Chen,et al.  Criminal network analysis and visualization , 2005, CACM.

[15]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[16]  Hock-Liew Eng,et al.  Networked digital library of theses and dissertations , 2005 .

[17]  Danah Boyd,et al.  Social network fragments: an interactive tool for exploring digital social connections , 2003, SIGGRAPH '03.

[18]  Danah Boyd,et al.  Vizster: visualizing online social networks , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[19]  Alfred Kobsa.  User Modeling and User-Adapted Interaction , 2005, User Modeling and User-Adapted Interaction.

[20]  Edward A. Fox,et al.  Interest-Based User Grouping Model for Collaborative Filtering in Digital Libraries , 2004, ICADL.

[21]  G. McCalla,et al.  Mining Implicit Ratings for Focused Collaborative Filtering for Paper Recommendations , 2003 .

[22]  James J. Thomas,et al.  Visualizing the non-visual: spatial analysis and interaction with information from text documents , 1995, Proceedings of Visualization 1995 Conference.

[23]  E.A. Fox,et al.  ETANA-DL: managing complex information applications - an archaeology digital library , 2004, Proceedings of the 2004 Joint ACM/IEEE Conference on Digital Libraries, 2004..

[24]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.