An Efficient Approach of GPU-accelerated Stochastic Gradient Descent Method for Matrix Factorization,ERICDATA高等教育知識庫
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篇名
An Efficient Approach of GPU-accelerated Stochastic Gradient Descent Method for Matrix Factorization
並列篇名
An Efficient Approach of GPU-accelerated Stochastic Gradient Descent Method for Matrix Factorization
作者 Feng LiYunming YeXutao Li
英文摘要
Matrix Factorization (MF) is a very effective tool for Collaborative Filtering (CF) in recommender systems. As a popular solver, Stochastic Gradient Descent (SGD) is widely utilized to find MF solutions for CF. However, SGD solver often suffers from a very slow optimization process, due to its large computation burden. How to speed up it becomes a very important research topic. One of the main techniques to the problem is partitioning the matrix to factorize into blocks and calculate the factorization parallelly with these blocks. In this paper, we would like to use the most modern computation resource Graphics Processing Unit (GPU) to speed up the partition-based computation. Though there are some studies on partition-based SGD with GPUs, due to the sparsity of matrices in real-life scenarios, these methods produce too many blank blocks, which will waste the GPU computing resources. In this paper, we propose a new method, which can avoid the problem and make use of GPUs more efficiently to speed up the SGD based MF solver.
起訖頁 1087-1098
關鍵詞 Collaborative filteringMatrix factorizationStochastic gradient descentGPU
刊名 網際網路技術學刊  
期數 201907 (20:4期)
出版單位 台灣學術網路管理委員會
DOI 10.3966/160792642019072004009   複製DOI
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