Library personalized recommendation service method based on improved association rules
| Published date | 17 September 2018 |
| Date | 17 September 2018 |
| Pages | 443-457 |
| DOI | https://doi.org/10.1108/LHT-06-2017-0120 |
| Author | Kaigang Yi,Tinggui Chen,Guodong Cong |
Library personalized
recommendation service
method based on improved
association rules
Kaigang Yi, Tinggui Chen and Guodong Cong
Zhejiang Gongshang University, Hangzhou, China
Abstract
Purpose –Nowadays, database management system has been applied in library management, and a great
number of data about readers’visiting history to resources have been accumulated by libraries. A lot of
important information is concealed behind such data. The purposeof this paper is to use a typical data mining
(DM) technology named an association rule mining model to find out borrowing rules of readers according to
their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase
utilization rate of data resources at library.
Design/methodology/approach –Association rule mining algorithm is applied to find out borrowing rules
of readers according to their borrowing records, and to recommend other booklists for them in a personalized
way, so as to increase utilization rate of data resources at library.
Findings –Through an analysis on record of book borrowing by readers, library manager can recommend
books that may be interested by a reader based on historical borrowing records or current book-borrowing
records of the reader.
Research limitations/implications –If many different categories of book-borrowing problems are
involved, it will result in large length of encoding as well as giant searching space. Therefore, future research
work may be considered in the following aspects: introduce clustering method; and apply association rule
mining method to procurement of book resources and layout of books.
Practical implications –The paper provides a helpful inspiration for Big Data mining and software
development, which will improve their efficiency and insight on users’behavior and psychology.
Social implications –The paper proposes a framework to help users understand others’behavior, which
will aid them better take part in group and community with more contribution and delightedness.
Originality/value –DM technology has been used to discover information concealed behind Big Data in
library; the library personalized recommendation problem has been analyzed and formulated deeply; and a
method of improved association rules combined with artificial bee colony algorithm has been presented.
Keywords Library management, Library users, Association rule, Data mining, Personalized recommendation,
Artificial bee colony
Paper type Research paper
Introduction
The volume of information resources is becoming increasingly bigger along with constant
development and extensive application of database technology, network technology, and
digital library technology, which has widely influence all walks of life such as business
process management (Liu et al., 2017), communication engineering (Vale et al., 2014),
academic library management (Charles and Chen, 2013), medical health (Zhang et al., 2017),
and so on. It is estimated that volume of information doubles in every 20 months globally
(Kim, 2002). However, it has become a problem regarding how to utilize these data
effectively. People often get “lost”in the face of such huge information resources, and feel
difficult to find information required in a rapid and effective way, resulting in scarcity of
knowledge in spite of possessing abundant data. Therefore, what is important, and also
what we need, is to find out information that is really valuable behind such data through
organization, analysis, and mining.
Library Hi Tech
Vol. 36 No. 3, 2018
pp. 443-457
© Emerald PublishingLimited
0737-8831
DOI 10.1108/LHT-06-2017-0120
Received 29 June 2017
Revised 13 November 2017
Accepted 13 November 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0737-8831.htm
This research is supported by National Social Science Fund Project (No. 16AGL009).
443
Personalized
recommendation
service
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