As it stands the Internets one size fits all approach to information retrieval presents the average user with a serious information overload problem. Adaptive hypermedia systems can provide a solution to this problem by learning about the implicit and explicit preferences of individual users and using this information to personalise information retrieval processes. We describe and evaluate a two-stage personalised information retrieval system that combines a server-side similarity-based retrieval component with a client-side case-based personalisation component. We argue that this combination has a number of benefits in terms of personalisation accuracy, computational cost, flexibility, security and privacy.
['Keith Bradley', 'Rachael Rafter', 'Barry Smyth']
Case-Based User Profiling for Content Personalisation