ISSN 0253-2778

CN 34-1054/N

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Research on collaborative recommendation algorithms based on parallel spectral clustering

  • China National Digital Switching System Engineering& Technological R&D Center,Zhengzhou 450002,China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.01.011
  • Received Date: 27 August 2015
  • Accepted Date: 29 September 2015
  • Rev Recd Date: 29 September 2015
  • Publish Date: 30 January 2016
    Abstract

  • Abstract

    With the increase of large-scale network data, scalability has become a key factor in the recommendation system. A new collaborative recommendation algorithm is thus based on MapReduce parallel spectral clustering was proposed. First, items are clustered using the improved parallel spectral clustering method; Then, based on the user collaborative recommendation algorithm and combined with the clustered items’ ratings, an improved calculation method for similar users is proposed to establish recommendation. The test results on the dataset show that the proposed algorithm can effectively reduce time complexity, which significantly improving its accuracy and efficiency.

    Abstract

    With the increase of large-scale network data, scalability has become a key factor in the recommendation system. A new collaborative recommendation algorithm is thus based on MapReduce parallel spectral clustering was proposed. First, items are clustered using the improved parallel spectral clustering method; Then, based on the user collaborative recommendation algorithm and combined with the clustered items’ ratings, an improved calculation method for similar users is proposed to establish recommendation. The test results on the dataset show that the proposed algorithm can effectively reduce time complexity, which significantly improving its accuracy and efficiency.
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    • References(10)
  • [1]
    罗辛, 欧阳元新, 熊璋, 等. 通过相似度支持度优化基于K近邻的协同过滤算法[J]. 计算机学报, 2010, 33(8): 1437-1445.
    Luo X, Ouyang Y X, Xiong Z, et al. The effext of similarity in K-nearest-neighborhood based collaborative filtering[J]. Chinese Journal of Computers, 2010, 33(8): 1437-1445.
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    范波, 程久军. 用户间多相似度协同过滤推荐算法[J]. 计算机科学, 2012, 39(1): 23-26.
    Fan B, Cheng J J. Collaborative filtering recommendation algorithm based on user’s multi-similarity[J]. Computer Sceience, 2012, 39(1): 23-26.
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    LiuH F, Hu Z, Mian A, et al. A new user similarity model to improve the accuracy of collaborative filtering[J]. Knowledge-Based Systems, 2014, 56(1): 156-166.
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    荣辉桂, 火生旭, 胡春华, 等. 基于用户相似度的协同过滤推荐算法[J]. 通信学报, 2014, 35(2): 16-24.
    Rong H G, Huo S X, Hu C H, et al. User similarity-based collaborative filtering recommendation algorithm[J]. Journal of Communications, 2014, 35(2): 16-24.
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    李华, 张宇, 孙俊华. 基于用户模糊聚类的协同过滤推荐研究[J]. 计算机科学, 2012, 39(12): 83-86.
    Li H, Zhang Y, Sun J H. Research on collaborative filtering recommendation based on user fuzzy clustering[J]. Computer Science,2012, 39(12): 83-86.
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    Ren X, Liu J L, Yu X, et al. ClusCite: Effective citation recommendation by information network-based clustering[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2014: 821-830.
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    Zhang D Q, Hsu C H, Chen M, et al. Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems[J]. IEEE Transactions on Emerging Topics in Computing, 2014, 2(2): 239-250.
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    Gong S J. A collaborative filtering recommendation algorithm based on user clustering and item clustering[J]. Journal of Software, 2010, 5(7): 745-752.
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    von Luxburg U. A tutorial on spectral clustering[J]. Statistics and Computing, 2007, 17(4): 395-416.
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    White T. Hadoop: The Definitive Guide[M]. New York: O'Reilly Media, 2012.
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    [1]
    罗辛, 欧阳元新, 熊璋, 等. 通过相似度支持度优化基于K近邻的协同过滤算法[J]. 计算机学报, 2010, 33(8): 1437-1445.
    Luo X, Ouyang Y X, Xiong Z, et al. The effext of similarity in K-nearest-neighborhood based collaborative filtering[J]. Chinese Journal of Computers, 2010, 33(8): 1437-1445.
    [2]
    范波, 程久军. 用户间多相似度协同过滤推荐算法[J]. 计算机科学, 2012, 39(1): 23-26.
    Fan B, Cheng J J. Collaborative filtering recommendation algorithm based on user’s multi-similarity[J]. Computer Sceience, 2012, 39(1): 23-26.
    [3]
    LiuH F, Hu Z, Mian A, et al. A new user similarity model to improve the accuracy of collaborative filtering[J]. Knowledge-Based Systems, 2014, 56(1): 156-166.
    [4]
    荣辉桂, 火生旭, 胡春华, 等. 基于用户相似度的协同过滤推荐算法[J]. 通信学报, 2014, 35(2): 16-24.
    Rong H G, Huo S X, Hu C H, et al. User similarity-based collaborative filtering recommendation algorithm[J]. Journal of Communications, 2014, 35(2): 16-24.
    [5]
    李华, 张宇, 孙俊华. 基于用户模糊聚类的协同过滤推荐研究[J]. 计算机科学, 2012, 39(12): 83-86.
    Li H, Zhang Y, Sun J H. Research on collaborative filtering recommendation based on user fuzzy clustering[J]. Computer Science,2012, 39(12): 83-86.
    [6]
    Ren X, Liu J L, Yu X, et al. ClusCite: Effective citation recommendation by information network-based clustering[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2014: 821-830.
    [7]
    Zhang D Q, Hsu C H, Chen M, et al. Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems[J]. IEEE Transactions on Emerging Topics in Computing, 2014, 2(2): 239-250.
    [8]
    Gong S J. A collaborative filtering recommendation algorithm based on user clustering and item clustering[J]. Journal of Software, 2010, 5(7): 745-752.
    [9]
    von Luxburg U. A tutorial on spectral clustering[J]. Statistics and Computing, 2007, 17(4): 395-416.
    [10]
    White T. Hadoop: The Definitive Guide[M]. New York: O'Reilly Media, 2012.
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