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• W2906331105 abstract "Recommender systems are used in most of nowadays applications. Providing real-time suggestions with high accuracy is considered as one of the most crucial challenges that face them. Matrix factorization (MF) is an effective technique for recommender systems as it improves the accuracy. Stochastic Gradient Descent (SGD) for MF is the most popular approach used to speed up MF. SGD is a sequential algorithm, which is not trivial to be parallelized, especially for large-scale problems. Recently, many researches have proposed parallel methods for parallelizing SGD. In this research, we propose GPU_MF_SGD, a novel GPU-based method for large-scale recommender systems. GPU_MF_SGD utilizes Graphics Processing Unit (GPU) resources by ensuring load balancing and linear scalability, and achieving coalesced access of global memory without preprocessing phase. Our method demonstrates 3.1X–5.4X speedup over the most state-of-the-art GPU method, CuMF_SGD." @default.
• W2906331105 created "2019-01-01" @default.
• W2906331105 creator A5003027550 @default.
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• W2906331105 date "2018-12-27" @default.
• W2906331105 modified "2023-09-25" @default.
• W2906331105 title "GPU_MF_SGD: A Novel GPU-Based Stochastic Gradient Descent Method for Matrix Factorization" @default.
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• W2906331105 doi "https://doi.org/10.1007/978-3-030-03405-4_18" @default.
• W2906331105 hasPublicationYear "2018" @default.
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