  We outline an inherent weakness of tensor factorization models when latent
factors are expressed as a function of side information and propose a novel
method to mitigate this weakness. We coin our method \textit{Kernel Fried
Tensor}(KFT) and present it as a large scale forecasting tool for high
dimensional data. Our results show superior performance against
\textit{LightGBM} and \textit{Field Aware Factorization Machines}(FFM), two
algorithms with proven track records widely used in industrial forecasting. We
also develop a variational inference framework for KFT and associate our
forecasts with calibrated uncertainty estimates on three large scale datasets.
Furthermore, KFT is empirically shown to be robust against uninformative side
information in terms of constants and Gaussian noise.
