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.
Highlights
The increasing popularity of mobile devices, such as laptops, mobile phones and personal digital assistants, and the advances in wireless networking technologies allow information to be accessed almost anywhere, at any time
The highlevel user preferences reflecting the long-term user interests are stored in a skeleton profile, which is managed by the server, while the low-level preferences representing the short-term user interests are stored in a detailed profile in the handset
It should be noted that the user is aware of the system’s personalization capabilities: (i) of automatically updating the high-level profile according to her long-term interests
Summary
The increasing popularity of mobile devices, such as laptops, mobile phones and personal digital assistants, and the advances in wireless networking technologies allow information to be accessed almost anywhere, at any time. The focus of this paper is to cover the personalization requirements of mobile users in the news domain, taking into account the user’s personal preferences and interests and attempting to preserve the privacy of the user preferences To this aim, our system architecture performs a management of a distributed user profile across client and server. The highlevel user preferences reflecting the long-term user interests are stored in a skeleton (high-level) profile, which is managed by the server, while the low-level preferences representing the short-term user interests are stored in a detailed (lowlevel) profile in the handset This distribution enables a twolevel matching process between the user profile and the news content, which uses semantic metadata extracted from the textual content and aims at the same time at a minimal computational and communication cost.
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