Distributed intelligent client-centric personalisation

Abstract

Personalisation is used extensively to improve user engagement, to optimise user experience and to enhance marketing and advertising online. While privacy has always been an issue in personalised websites, only recently have we seen a noticeable change in consumer’s behaviour’s. User’s are seeing breaches of the personal information harvested, stored and shared by content providers and increasingly adjusting privacy controls, thus negatively impacting the effectiveness of personalisation services. Client-Side personalisation (CSP) approaches offer a privacy-conscious solution, keeping the user data and user model on the client’s own device, allowing users to enjoy personalised content without compromising the privacy of their personal data. However, these solutions have significant problems with scalability and performance due to client-device resource limitations. With an ever-increasing demand for rich multimedia, particularly on more lightweight mobile devices, performance is critical to provide a seamless user experience. This research proposes a hybrid approach which we term Intelligent Client-Centric Personalisation (ICCP), this minimises the leakage of user data while enhancing performance through predictive webpage prefetching. This paper performs a comparative framework evaluation, comparing the ICCP framework performance with a typical client-server personalisation approach. It uses a large dataset of user interactions across three contrasting consumer websites, following case study based methodology. Evaluation shows that such a framework can realise the performance benefits of a client-server approach but with enhanced privacy and reduced personal data leakage.