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.
CITATION STYLE
Bradley, K., Rafter, R., & Smyth, B. (2000). Case-based user profiling for content personalisation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1892, pp. 62–72). Springer Verlag. https://doi.org/10.1007/3-540-44595-1_7
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