Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

11 Aug 2018Wei TanShiyu ChangLiana FongCheng LiZijun WangLiangliang Cao

Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction, word embedding, etc. While many problem-specific optimization techniques have been proposed, alternating least square (ALS) remains popular due to its general applicability e.g. easy to handle positive-unlabeled inputs, fast convergence and parallelization capability... (read more)

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