Abstract
Recommendation Systems are tools designed to help users to find items within a given domain, according to their own preferences expressed by means of a user profile. A general model for recommendation systems based on probabilistic graphical models is proposed in this paper. It is designed to deal with hierarchical domains, where the items can be grouped in a hierarchy, each item being only contained in another, more general item. The model makes decisions about which items in the hierarchy are more useful for the user, and carries out the necessary computations in a very efficient way.
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de Campos, L.M., Fernández-Luna, J.M., Gómez, M., Huete, J.F. (2005). A Decision-Based Approach for Recommending in Hierarchical Domains. In: Godo, L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science(), vol 3571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11518655_12
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DOI: https://doi.org/10.1007/11518655_12
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