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International Journal of Digital Multimedia Broadcasting
Volume 2008, Article ID 863613, 21 pages
Research Article

Two-Level Automatic Adaptation of a Distributed User Profile for Personalized News Content Delivery

1Centre for Research and Technology Hellas (CERTH), Informatics and Telematics Institute (ITI), 1st Km Thermi-Panorama Road, Thessaloniki 57001, Greece
2Motorola Labs, Motorola Ltd., Jays Close, Viables Industrial Estate, Basingstoke, Hampshire, RG22 4PD, UK
3Motorola Labs, Parc Les Algorithmes, Saint Aubin, 91193 Gif sur Yvette Cedex, France

Received 28 February 2008; Accepted 24 June 2008

Academic Editor: Harald Kosch

Copyright © 2008 Maria Papadogiorgaki et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a distributed client-server architecture for the personalized delivery of textual news content to mobile users. The user profile consists of two separate models, that is, the long-term interests are stored in a skeleton profile on the server and the short-term interests in a detailed profile in the handset. The user profile enables a high-level filtering of available news content on the server, followed by matching of detailed user preferences in the handset. The highest rated items are recommended to the user, by employing an efficient ranking process. The paper focuses on a two-level learning process, which is employed on the client side in order to automatically update both user profile models. It involves the use of machine learning algorithms applied to the implicit and explicit user feedback. The system's learning performance has been systematically evaluated based on data collected from regular system users.