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 low- level 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.