A Novel Data Mining Testbed for User Centred Modelling and Personalisation of Digital Library Services

Maram ALMAGHRABI, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

Digital libraries can provide information services for users with diverse needs. Due to a large amount of data that exists in digital library systems, including text and multimedia resources, with different cohorts of users, and the challenges with existing digital library systems in terms of maintaining privacy and confidentiality, it is very difficult to provide personalised library services and improved user experience. However, novel data mining algorithms based on automatic user segmentation and borrowing behaviour modelling can leverage the relationship between users and borrowing records, to improve the library services. In this paper, we present an automatic approach for personalising the resources by segmenting the users and their preferences, based on a data mining strategy, involving, the classification based on Nave Bayes, J48 and K-Nearest Neighbours Classification (K-NN) and using open source technology tools for evaluating the personalisation and improved user experience with digital library services.

Original languageEnglish
Title of host publicationProceedings - 13th IEEE International Conference on eScience, eScience 2017
Place of PublicationAuckland, New Zealand
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages434-435
Number of pages2
ISBN (Electronic)9781538626863
ISBN (Print)9781538626863
DOIs
Publication statusPublished - 14 Nov 2017
Event2017 IEEE 13th International Conference on e-Science (e-Science) - Auckland, Auckland, New Zealand
Duration: 24 Oct 201727 Oct 2017
http://escience2017.org.nz/ (Conference Link)

Publication series

NameProceedings - 13th IEEE International Conference on eScience, eScience 2017

Conference

Conference2017 IEEE 13th International Conference on e-Science (e-Science)
CountryNew Zealand
CityAuckland
Period24/10/1727/10/17
Internet address

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