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A graphical model formulation of collaborative filtering neighbourhood methods with fast maximum entropy training

Defazio, Aaron; Caetano, Tiberio


Item neighbourhood methods for collaborative filtering learn a weighted graph over the set of items, where each item is connected to those it is most similar to. The prediction of a user's rating on an item is then given by that rating of neighbouring items, weighted by their similarity. This paper presents a new neighbourhood approach which we call item fields, whereby an undirected graphical model is formed over the item graph. The resulting prediction rule is a simple generalization of the...[Show more]

CollectionsANU Research Publications
Date published: 2012
Type: Conference paper
Source: Proceedings of the 29th International Conference on Machine Learning, ICML 2012


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