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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Coordinating negotiations in data-intensive collaborative working environments using an agent-based model-driven platform. Enterprise Information Systems.

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This paper presents a distributed client-server architecture for the personalized delivery of textual news content to mobile users. The user profile is distributed across client and server, enabling a high-level filtering of available content on the server, followed by matching of detailed user preferences on the handset. The high-level user preferences are stored in a skeleton profile on the server, and the lowlevel preferences in a detailed user profile on the handset. A learning process for the detailed user profile is employed on the handset exploiting the implicit and explicit user feedback. The system's learning performance has been evaluated based on data collected from regular system users.