Abstract
This paper presents an approach for finding optimal presentation sequences in conversational Recommender Systems. The strategies simultaneously pursuit the goals of acquainting the user with the different possibilities, successfully accomplishing the task in the shortest possible time, and obtaining an accurate user model. The approach is modeled as an MDP where the states include belief states about the acceptability of the different alternatives, modeled as Bayesian networks.
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Bertomeu Castelló, N. (2012). Finding Optimal Presentation Sequences for a Conversational Recommender System. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31724-8_34
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DOI: https://doi.org/10.1007/978-3-642-31724-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31723-1
Online ISBN: 978-3-642-31724-8
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