Using data mining to improve digital library services
| Published date | 16 November 2010 |
| Date | 16 November 2010 |
| Pages | 829-843 |
| DOI | https://doi.org/10.1108/02640471011093525 |
| Author | Ana Kovacevic,Vladan Devedzic,Viktor Pocajt |
| Subject Matter | Information & knowledge management,Library & information science |
Using data mining to improve
digital library services
Ana Kovacevic
Faculty of Security Studies, University of Belgrade, Belgrade, Serbia
Vladan Devedzic
Department ofSoftware Engineering, FON – School of Business Administration,
University of Belgrade, Belgrade, Serbia, and
Viktor Pocajt
Faculty of Technology and Metallurgy, University of Belgrade,
Belgrade, Serbia
Abstract
Purpose – This paper aims to propose a solution for recommending digital library services based on
data mining techniques (clustering and predictive classification).
Design/methodology/approach – Data mining techniques are used to recommend digital library
services based on the user’s profile and search history. First, similar users were clustered together,
based on their profiles and search behavior. Then predictive classification for recommending
appropriate services to them was used. It has been shown that users in the same cluster have a high
probability of accepting similar services or their patterns.
Findings – The results indicate that k-means clustering and Naive Bayes classification may be used
to improve the accuracy of service recommendation. The overall accuracy is satisfying, while average
accuracy depends on the specific service. The results were better for frequently occurring services.
Research limitations/implications – Datasets were used from the KOBSON digital library. Only
clustering and predictive classification was applied. If the correlation between the service and the
institution were higher, it would have better accuracy.
Originality/value – The paper applied different and efficient data mining techniques for clustering
digital library users based on their profiles and their search behavior, i.e. users’ interaction with library
services, and obtain user patterns with respect to the library services they use. A digital library may
apply this approach to offer appropriate services to new users more easily. The recommendations will
be based on library items that similar users have already found useful.
Keywords Digital libraries,Databases, Serbia, Data handling,Service delivery
Paper type Research paper
1. Introduction
Although we are today overwhelmed with data, techniques for finding appropriate
information are mostly based on syntax search or low-level multimedia features. For
improving search results in interaction with digital libraries (DLs), other more
intelligent techniques should be used, based on both top-down knowledge creation (e.g.
ontologies, user modeling) and bottom-up automated knowledge extraction (e.g. data
mining, web mining) (Chen, 2003).
Valuable information extracted from the collection of DL data can be integrated into
the library’s strategy, and can be used to improve library search (Chang and Chen,
2006). For an effective design of systems and particularly to help users to find
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0264-0473.htm
Using data
mining to
improve services
829
Received 2 September 2009
Revised 11 November 2009
Accepted 16 November 2009
The Electronic Library
Vol. 28 No. 6, 2010
pp. 829-843
qEmerald Group Publishing Limited
0264-0473
DOI 10.1108/02640471011093525
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