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.
@article{arxiv.1510.07035,
title = {Fast Latent Variable Models for Inference and Visualization on Mobile Devices},
author = {Joseph W Robinson and Aaron Q Li},
journal= {arXiv preprint arXiv:1510.07035},
year = {2015}
}