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Improving the quality of the personalized electronic program guide

O'Sullivan, Dermot and Wilson, David C. and Smyth, Barry and McDonald, Kieran and Smeaton, Alan F. (2004) Improving the quality of the personalized electronic program guide. User Modeling and User-Adapted Interaction, 14 (1). pp. 5-36. ISSN 0924-1868

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As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems—PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system.

Item Type:Article (Published)
Additional Information:The original publication is available at
Uncontrolled Keywords:Personalization; Data Mining; Digital TV; Collaborative Filtering; Similarity Maintenance; Case-based Reasoning;
Subjects:Engineering > Telecommunication
Computer Science > Artificial intelligence
Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Publisher:Springer Netherlands
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Enterprise Ireland
ID Code:204
Deposited On:04 Mar 2008 by DORAS Administrator. Last Modified 06 May 2010 15:36

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