Abstract
An important problem in providing personalised recommendations is how to determine when the sample items are representative of the user's long-term preferences (user profile) and when more sample items must be collected before a profile pattern may be identified. In this paper, we present an algorithm to determine if a particular sample set for a user is sufficient to provide personalised recommendations from a large collection. Our algorithm identifies features of items that the user likes, and then determines if these features have sufficient discrimination to extract a small fraction of the collection from which recommendations are provided. The determination is based on Bayesian theory of probability and has the advantage that sample sets can be assessed quickly for their generalisation ability. We demonstrate the usefulness of the algorithm through an empirical evaluation on data collected about movie preferences.
The permission of the Director of Telstra Research Laboratories, to publish this work is gratefully acknowledged.
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© 1997 Springer-Verlag Berlin Heidelberg
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Raskutti, B., Beitz, A. (1997). Sample set assessment for providing personalised recommendations. In: Sattar, A. (eds) Advanced Topics in Artificial Intelligence. AI 1997. Lecture Notes in Computer Science, vol 1342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63797-4_89
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DOI: https://doi.org/10.1007/3-540-63797-4_89
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