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

Maria Papadogiorgaki | Zendy; Vasileios Papastathis | Zendy; Evangelia Nidelkou | Zendy; Simon N. Waddington | Zendy; Ben Bratu | Zendy; Myriam Ribière | Zendy; Ioannis Kompatsiaris | Zendy

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

Author(s) -

Maria Papadogiorgaki,

Vasileios Papastathis,

Evangelia Nidelkou,

Simon N. Waddington,

Ben Bratu,

Myriam Ribière,

Ioannis Kompatsiaris

Publication year - 2008

Publication title -

international journal of digital multimedia broadcasting

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.164

H-Index - 17

eISSN - 1687-7586

pISSN - 1687-7578

DOI - 10.1155/2008/863613

Subject(s) - computer science , user profile , content adaptation , handset , adaptation (eye) , ranking (information retrieval) , server side , user modeling , process (computing) , client side , matching (statistics) , content delivery , scheme (mathematics) , information retrieval , human–computer interaction , world wide web , user interface , computer network , ubiquitous computing , operating system , mathematical analysis , statistics , physics , mathematics , optics

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.

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