Distributed User Modeling for Personalized News Delivery in Mobile Devices

Abstract

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.

Research Issues in Personalization of Mobile Services

“…This approach also applies policies and rules to handle conflicts in data. Another distributed approach [25] is used to provide personalized news delivery on mobile devices.…”

“…It has merged the user modeling server and mediation role to allow users to explicitly select the information to disclose to a particular service provider at a time. Similarly, [25] has suggested distributed approach which stores permanent parts of user model on a server and short term user model on the user's personal device (i.e. ; a mobile phone).…”