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 language | English |
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| Title of host publication | Proceedings - 13th IEEE International Conference on eScience, eScience 2017 |
| Place of Publication | Auckland, New Zealand |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 434-435 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781538626863 |
| ISBN (Print) | 9781538626863 |
| DOIs | |
| Publication status | Published - 14 Nov 2017 |
| Event | 2017 IEEE 13th International Conference on e-Science (e-Science) - Auckland, Auckland, New Zealand Duration: 24 Oct 2017 → 27 Oct 2017 http://escience2017.org.nz/ (Conference Link) |
Publication series
| Name | Proceedings - 13th IEEE International Conference on eScience, eScience 2017 |
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Conference
| Conference | 2017 IEEE 13th International Conference on e-Science (e-Science) |
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| Country/Territory | New Zealand |
| City | Auckland |
| Period | 24/10/17 → 27/10/17 |
| Internet address |
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