70 citations · 92 across the 3 of their papers we have counts for
3 papers
cs.AI2013★ 5 cited
Mixture Approximations to Bayesian Networks
Volker Tresp, Michael Haft, Reimar Hofmann
Structure and parameters in a Bayesian network uniquely specify the probability distribution of the modeled domain. The locality of both structure and probabilistic information are…
cs.LG2012★ 70 cited
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
Kai Yu, Anton Schwaighofer, Volker Tresp +2
Collaborative filtering (CF) and content-based filtering (CBF) have widely been used in information filtering applications. Both approaches have their strengths and weaknesses whic…
cs.AI2012★ 17 cited
Infinite Hidden Relational Models
Zhao Xu, Volker Tresp, Kai Yu +1
In many cases it makes sense to model a relationship symmetrically, not implying any particular directionality. Consider the classical example of a recommendation system where the…