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2004
ACM

A nonparametric hierarchical bayesian framework for information filtering

9 years 2 months ago
A nonparametric hierarchical bayesian framework for information filtering
Information filtering has made considerable progress in recent years.The predominant approaches are content-based methods and collaborative methods. Researchers have largely concentrated on either of the two approaches since a principled unifying framework is still lacking. This paper suggests that both approaches can be combined under a hierarchical Bayesian framework. Individual content-based user profiles are generated and collaboration between various user models is achieved via a common learned prior distribution. However, it turns out that a parametric distribution (e.g. Gaussian) is too restrictive to describe such a common learned prior distribution. We thus introduce a nonparametric common prior, which is a sample generated from a Dirichlet process which assumes the role of a hyper prior. We describe effective means to learn this nonparametric distribution, and apply it to learn users’ information needs. The resultant algorithm is simple and understandable, and offers a ...
Kai Yu, Volker Tresp, Shipeng Yu
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where SIGIR
Authors Kai Yu, Volker Tresp, Shipeng Yu
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