  While matrix factorisation models are ubiquitous in large scale
recommendation and search, real time application of such models requires inner
product computations over an intractably large set of item factors. In this
manuscript we present a novel framework that uses the inverted index
representation to exploit structural properties of sparse vectors to
significantly reduce the run time computational cost of factorisation models.
We develop techniques that use geometry aware permutation maps on a tessellated
unit sphere to obtain high dimensional sparse embeddings for latent factors
with sparsity patterns related to angular closeness of the original latent
factors. We also design several efficient and deterministic realisations within
this framework and demonstrate with experiments that our techniques lead to
faster run time operation with minimal loss of accuracy.
