The goal of personalization in information retrieval is to tailor the search engine results to the specific goals, preferences and general interests of the users. We propose a no...
Hierarchical probabilistic modeling of discrete data has emerged as a powerful tool for text analysis. Posterior inference in such models is intractable, and practitioners rely on...
We investigate the problem of learning a widely-used latent-variable model – the Latent Dirichlet Allocation (LDA) or “topic” model – using distributed computation, where ...
David Newman, Arthur Asuncion, Padhraic Smyth, Max...
In this paper we propose a novel document retrieval model in which text queries are augmented with multi-dimensional taxonomy restrictions. These restrictions may be relaxed at a ...
Marcus Fontoura, Vanja Josifovski, Ravi Kumar, Chr...
This paper describes nonparametric Bayesian treatments for analyzing records containing occurrences of items. The introduced model retains the strength of previous approaches that...