Computer Science ›› 2019, Vol. 46 ›› Issue (8): 106-110.doi: 10.11896/j.issn.1002-137X.2019.08.017

• HPC China 2018 • Previous Articles     Next Articles

GPU-accelerated Non-negative Matrix Factorization-based Parallel Collaborative Filtering Recommendation Algorithm

KANG Lin-yao, TANG Bing, XIA Yan-min, ZHANG Li   

  1. (School of Computer Science and Engineering,Hunan University of Science and Technology,Xiangtan,Hunan 411201,China)
  • Received:2018-09-15 Online:2019-08-15 Published:2019-08-15

Abstract: Collaborative filtering (CF) is widely used in recommendation systems.However,with the increase of user and item number,the efficiency of collaborative filtering algorithm and the correctness of the result will be greatly reduced.To solve this problem,this paper proposed a GPU-accelerated non-negative matrix factorization(NMF)-based parallel collaborative filtering algorithm.By utilizing the advantages of data dimensionality reduction and feature extraction of NMF,as well as the multi-core parallel computing mode of CUDA,dimension reduction and user similarity are realized.The proposed algorithm improves the recommendation accuracy and also reduces the computational cost,which can better solve the sparseness and scalability of CF-based recommendation system,and generate accurate and persona-lized recommendations quickly.The new algorithm was evaluated on a NVIDIA CUDA device using real MovieLens datasets.Experimental results show that,NMF-based collaborative filtering outperforms traditional User-based and Item-based CF with higher processing speed and higher accuracy recommendations

Key words: Collaborative filtering, Non-negative matrix factorization, GPU, Recommendation algorithm

CLC Number: 

  • TP391
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