2016/05/16 by Avradeep Bhowmik, Nathan Liu, Bhowmik, Avradeep +7
Computer Science · Mathematics · #Advanced Graph Neural Networks #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Recommender Systems and Techniques #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1605.04764
AISTATS 2016, 13 pages, 5 figures
arxiv created 2016/05/16 · openalex publication_date 2016/05/16 · arxiv updated 2016/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.