No abstract

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.

A major theme of Information Science and Technology research is the study of personalization. The key issue of personalization is the problem of understanding human behaviour and its simulation by machines, in a sense that machines can treat users as individuals with respect to their distinct personalities, preferences, goals and so forth. The general fields of research in personalization are user modeling and adaptive systems, which can be traced back to the late 70s, with the use of models of agents by Perrault, Allen, and Cohen (1978) and the introduction of stereotypes by Rich (1979). With the wide progress in hardware and telecom­munications technologies that has led to a vast increase in the services, volume and multimodality (text and multimedia) of content, in the last decade, the need for personalization systems is critical, in order to enable both consumers to manage the volume and complexity of available information and vendors to be competitive in the market.