Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2002

Abstract

In this paper, we propose a data mining approach to recommending new library books that have never been rated or borrowed by users. In our problem context, users are characterized by their demographic attributes, and concept hierarchies can be defined for some of these demographic attributes. Books are assigned to the base categories of a taxonomy. Our goal is therefore to identify the type of users interested in some specific type of books. We call such knowledge generalized profile association rules. In this paper, we propose a new definition of rule interestingness to prune away rules that are redundant and not useful in book recommendation. We have developed a new algorithm for efficiently discovering generalized profile association rules from a circulation database. It is noted that generalized profile association rules can be applied to other kinds of applications, including e-commerce.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

Digital Libraries: People, Knowledge, and Technology: 5th International Conference on Asian Digital Libraries, ICADL 2002 Singapore, December 11–14, 2002 Proceedings

Volume

2555

First Page

229

Last Page

240

ISBN

9783540362272

Identifier

10.1007/3-540-36227-4_23

Publisher

Springer Verlag

City or Country

Singapore

Additional URL

http://doi.org/10.1007/3-540-36227-4_23

Download

Share

COinS