Users' Book-Loan Behaviors Analysis and Knowledge Dependency Mining

Book-loan is the most important library service. Studying users' book-loan behavior patterns can help libraries to provide more proactive services. Based on users' book-loan history in a university library, we could build a book-borrowing network between users and books. Furthermore, users who borrow the same books are linked together. The users and links then form a co-borrowing network which can be regarded as a knowledge sharing network. Both the book-borrowing network and the co-borrowing network can be used to study users' bookloan behavior patterns. This paper presents a study in analyzing users' book-loan behaviors and mining knowledge dependency between schools and degrees in Peking University. The mining work is based on the book-borrowing network and its corresponding co-borrowing network. To the best of our knowledge, it is the first work to mine knowledge dependency in digital library domain.

[1]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Herman J. Loether,et al.  Descriptive and inferential statistics: An introduction , 1980 .

[3]  Scott Nicholson,et al.  The basis for bibliomining: Frameworks for bringing together usage-based data mining and bibliometrics through data warehousing in digital library services , 2006, Inf. Process. Manag..

[4]  Lada A. Adamic,et al.  Knowledge sharing and yahoo answers: everyone knows something , 2008, WWW.

[5]  Kim Guenther Applying Data Mining Principles to Library Data Collection. , 2000 .

[6]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[7]  Mark H. Chignell,et al.  Identifying communities in blogs: roles for social network analysis and survey instruments , 2007, Int. J. Web Based Communities.

[8]  Yang Lu,et al.  Analyzing user's book-loan behaviors in Peking university library from social network perspective , 2009, JCDL '09.

[9]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[10]  Matthew Richardson,et al.  Yes, there is a correlation: - from social networks to personal behavior on the web , 2008, WWW.

[11]  Xin Li,et al.  Tag-based social interest discovery , 2008, WWW.

[12]  Johan Bollen,et al.  Usage Analysis for the Identification of Research Trends in Digital Libraries , 2003, D Lib Mag..

[13]  Lada A. Adamic,et al.  A social network caught in the Web , 2003, First Monday.

[14]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[15]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[16]  Yang Lu,et al.  Community Discovery Based on Social Actors' Interests and Social Relationships , 2008, 2008 Fourth International Conference on Semantics, Knowledge and Grid.