A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training

Understand

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 classical approaches, which takes into account non-local information in the graph, allowing its best results to be obtained when using drastically fewer edges than other neighbourhood approaches.