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
[1] DENG A L,ZHU Y Y,SHI B L.Collaborative filtering Recommendation algorithm based on project score prediction [J].Journal of Software,2003,14(9):1621-1628.(in Chinese) 邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1628.
[2] LEE D D H,SEUNG S.Learning the parts of objects by nonnegative matrix factorization [J].Nature,1999,401(6755):788-791.
[3] LIU J F,GUO L.Comparison and analysis of several matrix multiplications on CPU and GPU [J].Computer Engineering and Applications,2011,47(19):9-11,23.(in Chinese) 刘进锋,郭雷.CPU与GPU上几种矩阵乘法的比较与分析[J].计算机工程与应用,2011,47(19):9-11,23.
[4] CHENG H,ZHANG Y Q,ZHANG X Y,et al.Implementation and performance analysis of CPU-GPU parallel matrix multiplication [J].Computer Engineering,2010,36(13):24-26,29.(in Chinese) 程豪,张云泉,张先轶,等.CPU-GPU 并行矩阵乘法的实现与性能分析[J].计算机工程,2010,36(13):24-26,29.
[5] LIU W X,ZHENG N N,YOU Q B.Non-negative matrix factorization and its application in pattern recognition [J].Chinese Science Bulletin,2006,51(3):241-250.(in Chinese) 刘维湘,郑南宁,游屈波.非负矩阵分解及其在模式识别中的应用[J].科学通报,2006,51(3):241-250.
[6] WANG K J,ZUO C T.Research progress of feature extraction techniques for nonnegative matrix factorization [J].Application Research of Computers,2014,31(4):970-975.(in Chinese) 王科俊,左春婷.非负矩阵分解特征提取技术的研究进展[J].计算机应用研究,2014,31(4):970-975.
[7] CHEN Y H,REGE M,DONG M,et al.Non-negative matrix factorization for semi-supervised data clustering [J].Knowledge and Information Systems,2008,17(3):355-379.
[8] KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems [J].IEEE Computer,August 2009,42(8):40-49.
[9] YANG Y,XIANG Y,XIONG L.Collaborative filtering recommendation algorithm based on matrix decomposition and user neighbor model[J].Computer Application,2012,32(2):395-398.(in Chinese) 杨阳,向阳,熊磊.基于矩阵分解与用户近邻模型的协同过滤推荐算法[J].计算机应用,2012,32(2):395-398.
[10] LI G,LI L.Collaborative filtering algorithm based on matrix decomposition[J].Computer Engineering and Applications,2011,47(30):4-7.(in Chinese) 李改,李磊.基于矩阵分解的协同过滤算法[J].计算机工程与应用,2011,47(30):4-7.
[1] WANG Rui-ping, JIA Zhen, LIU Chang, CHEN Ze-wei, LI Tian-rui. Deep Interest Factorization Machine Network Based on DeepFM [J]. Computer Science, 2021, 48(1): 226-232.
[2] MA Li-bo, QIN Xiao-lin. Topic-Location-Category Aware Point-of-interest Recommendation [J]. Computer Science, 2020, 47(9): 81-87.
[3] LI Yu-rong, LIU Jie, LIU Ya-lin, GONG Chun-ye, WANG Yong. Parallel Algorithm of Deep Transductive Non-negative Matrix Factorization for Speech Separation [J]. Computer Science, 2020, 47(8): 49-55.
[4] LIU Jun-liang, LI Xiao-guang. Techniques for Recommendation System:A Survey [J]. Computer Science, 2020, 47(7): 47-55.
[5] LUO Jia-lei and MENG Li-min. Signal Timing Scheme Recommendation Algorithm Based on Intersection Similarity [J]. Computer Science, 2020, 47(6A): 66-69.
[6] MA Hai-Jiang. Recommendation Algorithm Based on Convolutional Neural Network and Constrained Probability Matrix Factorization [J]. Computer Science, 2020, 47(6A): 540-545.
[7] LIU Shi-fang, ZHAO Yong-hua, YU Tian-yu, HUANG Rong-feng. Efficient Implementation of Generalized Dense Symmetric Eigenproblem StandardizationAlgorithm on GPU Cluster [J]. Computer Science, 2020, 47(4): 6-12.
[8] ZUO Xian-yu, ZHANG Zhe, SU Yue-han, LIU Yang, GE Qiang, TIAN Jun-feng. Extraction Algorithm of NDVI Based on GPU Multi-stream Parallel Model [J]. Computer Science, 2020, 47(4): 25-29.
[9] ZHU Lei, HU Qin-han, ZHAO Lei, YANG Ji-wen. Collaborative Filtering Algorithm Based on Rating Preference and Item Attributes [J]. Computer Science, 2020, 47(4): 67-73.
[10] ZHAO Nan, PI Wen-chao, XU Chang-qiao. Video Recommendation Algorithm for Multidimensional Feature Analysis and Filtering [J]. Computer Science, 2020, 47(4): 103-107.
[11] FENG Chen-jiao,LIANG Ji-ye,SONG Peng,WANG Zhi-qiang. New Similarity Measure Based on Extremely Rating Behavior [J]. Computer Science, 2020, 47(2): 31-36.
[12] WU Lei,YUE Feng,WANG Han-ru,WANG Gang. Academic Paper Recommendation Method Combined with Researcher Tag [J]. Computer Science, 2020, 47(2): 51-57.
[13] HUANG Chao-ran, GAN Yong-shi. Balance Between Preference and Universality Based on Explicit Feedback Collaborative Filtering [J]. Computer Science, 2020, 47(11A): 471-473.
[14] ZHOU Bo. Bipartite Network Recommendation Algorithm Based on Semantic Model [J]. Computer Science, 2020, 47(11A): 482-485.
[15] ZHOU Chang, LI Xiang-li, LI Qiao-lin, ZHU Dan-dan, CHEN Shi-lian, JIANG Li-rong. Sparse Non-negative Matrix Factorization Algorithm Based on Cosine Similarity [J]. Computer Science, 2020, 47(10): 108-113.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .