An architecture for evolving the electronic programme guide for online viewing

Al Mohammed, EA ORCID: 0000-0002-8549-8279 2017, An architecture for evolving the electronic programme guide for online viewing , PhD thesis, University of Salford.

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Abstract

Watching television and video content is changing towards online viewing due to the proliferation of content providers and the prevalence of high speed broadband. This trend is coupled to an acceleration in the move to watching content using non-traditional viewing devices such as laptops, tablets and smart phones. This, in turn, poses a problem for the viewer in that it is becoming increasingly difficult to locate those programmes of interest across such a broad range of providers.

In this thesis, an architecture of a generic cloud-based Electronic Programme Guide (EPG) system has been developed to meet this challenge. The key feature of this architecture is the way in which it can access content from all of the available online content providers and be personalized depending on the viewer’s preferences and interests, viewing device, internet connection speed and their social network interactions. Fundamental to its operation is the translation of programme metadata adopted by each provider into a unified format that is used within the core system. This approach ensures that the architecture is extensible, being able to accommodate any new online content provider through the addition of a small tailored search agent module. The EPG system takes the programme as its core focus and provides a single list of recommendations to each user regardless of their origins.

A prototype has been developed in order to validate the proposed system and evaluate its operation. Results have been obtained through a series of user trials to assess the system’s effectiveness in being able to extract content from several sources and to produce a list of recommendations which match the user’s preferences and context.

Results show that the EPG is able to offer users a single interface to online television and video content providers and that its integration with social networks ensures that the recommendation process is able to match or exceed the published results from comparable, but more constrained, systems.

Item Type: Thesis (PhD)
Contributors: Linge, N (Supervisor)
Schools: Schools > School of Computing, Science and Engineering
Depositing User: Emad Abdulrazzaq Al Mohammed
Date Deposited: 20 Feb 2018 12:59
Last Modified: 20 Mar 2018 01:38
URI: http://usir.salford.ac.uk/id/eprint/44177

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