Generating personalised service recommendations

Huczynski, Gregory (2004) Generating personalised service recommendations. PhD thesis, University of Glasgow.

Full text available as:
[thumbnail of scanned version of the original print thesis] PDF (scanned version of the original print thesis)
Download (16MB)
Printed Thesis Information: https://eleanor.lib.gla.ac.uk/record=b2251884

Abstract

In the context of service-oriented computing, the issue of service selection is an important one: how can a consumer find and choose a single, appropriate service of the required type, given the mass of services potentially available on a network? By using a service discovery mechanism (the focus of current service selection research), a consumer is able to obtain an unordered list of services which match explicitly specified requirements, from which he must select the service he considers most appropriate. However, formulating the original service request and selecting a service from the returned list are both challenging tasks, particularly for a consumer in unknown circumstances, with unknown services available. This research is thus concerned with the investigation, development and evaluation of a general design for a system that can provide a personalised service recommendation of appropriate services to a requesting consumer. The personalised service recommendation is generated through the assessment of past service selections/usage. A design-adhering prototype has been demonstrated to generate effective personalised service recommendations in a real-world scenario.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Computer science.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Colleges/Schools: College of Science and Engineering > School of Computing Science
Supervisor's Name: Dickman, Peter and Gray, Phil
Date of Award: 2004
Depositing User: Enlighten Team
Unique ID: glathesis:2004-74056
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 23 Sep 2019 15:33
Last Modified: 17 Aug 2021 09:08
Thesis DOI: 10.5525/gla.thesis.74056
URI: https://theses.gla.ac.uk/id/eprint/74056

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year