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

Large Scale Tensor Regression using Kernels and Variational Inference