Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP...
Many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings. In this paper we present the Prob...
Previous attempts at identifying translational equivalents in comparable corpora have dealt with very large `general language' corpora and words. We address this task in a sp...
In the KL divergence framework, the extended language modeling approach has a critical problem estimating a query model, which is the probabilistic model that encodes user’s inf...
We present an unsupervised word segmentation model for machine translation. The model uses existing monolingual segmentation techniques and models the joint distribution over sour...