  In this project we outline Vedalia, a high performance distributed network
for performing inference on latent variable models in the context of Amazon
review visualization. We introduce a new model, RLDA, which extends Latent
Dirichlet Allocation (LDA) [Blei et al., 2003] for the review space by
incorporating auxiliary data available in online reviews to improve modeling
while simultaneously remaining compatible with pre-existing fast sampling
techniques such as [Yao et al., 2009; Li et al., 2014a] to achieve high
performance. The network is designed such that computation is efficiently
offloaded to the client devices using the Chital system [Robinson & Li, 2015],
improving response times and reducing server costs. The resulting system is
able to rapidly compute a large number of specialized latent variable models
while requiring minimal server resources.
