ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Collaborative filtering recommendation algorithm based on nearest neighbor clustering

  • College of Information Science and Engineering , Hunan University, Changsha 410082, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.09.004
  • Received Date: 01 March 2016
  • Accepted Date: 17 September 2016
  • Rev Recd Date: 17 September 2016
  • Publish Date: 30 September 2016
    Abstract

  • Abstract

    With the increasing number of users and items in recommender systems, designing a scalable algorithm becomes a big challenge for recommendation systems. However, many recommendation algorithms and the improved algorithms proposed thus far have focused on improving recommendation quality, resulting in shortcomings such as lower recommendation efficiency and running time consumption as the system increases in scale. To address the problem of scalability, a collaborative filtering recommendation algorithm based on nearest neighbor clustering was proposed. Firstly, the k-means algorithm was utilized to place similar scores into the same cluster, which was used to build the user clustering model. Then, it picked out the active users’ nearest neighbor clusters from the clustering model and treats them as a retrieval space. Finally, the nearest neighbors of an active user are found according to the retrieval space, and the recommendation to the active user was given. Experimental results show that the algorithm proposed in this paper not only significantly improves the response speed of the recommendation system online but also maintains a high accuracy.

    Abstract

    With the increasing number of users and items in recommender systems, designing a scalable algorithm becomes a big challenge for recommendation systems. However, many recommendation algorithms and the improved algorithms proposed thus far have focused on improving recommendation quality, resulting in shortcomings such as lower recommendation efficiency and running time consumption as the system increases in scale. To address the problem of scalability, a collaborative filtering recommendation algorithm based on nearest neighbor clustering was proposed. Firstly, the k-means algorithm was utilized to place similar scores into the same cluster, which was used to build the user clustering model. Then, it picked out the active users’ nearest neighbor clusters from the clustering model and treats them as a retrieval space. Finally, the nearest neighbors of an active user are found according to the retrieval space, and the recommendation to the active user was given. Experimental results show that the algorithm proposed in this paper not only significantly improves the response speed of the recommendation system online but also maintains a high accuracy.
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    • References(14)
  • [1]
    ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Transaction on Knowledge and Data Engineering, 2005, 17(6):734-749.
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    MENG X W, HU X, WANG L C, et al. Mobile recommender systems and their applications[J]. Journal of Software, 2013, 24(1):91-108.
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    CONSTANTINOPOULOS C, LIKAS A. Unsupervised learning of Gaussian mixtures based on variational component splitting[J]. IEEE Transactions on Neural Networks, 2007, 18(3): 745-755.
    [5]
    CACHEDA F, CARNEIRO V, FERNNDEZ D, et al. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems[J]. ACM Transactions on the Web, 2011, 5(1): 161-171.
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    NEWTON J, GREINER R. Hierarchical probabilistic relational models for collaborative filtering[C]// Proceedings of the 21st International Conference on Machine Learning—Workshop on Statistical Relational Learning. New York: ACM Press, 2004: 249-163.
    [7]
    BELL R, KOREN Y, VOLINSKY C. Modeling relationships at multiple scales to improve accuracy of large recommender systems[C]// Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose: ACM Press, 2007: 95-104.
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    DAKHEL G M, MAHDAVI M. A new collaborative filtering algorithm using K-means clustering and neighbors’ voting[C]// Proceedings of the 11th International Conference on Hybrid Intelligent Systems. Melacca, Malaysia: IEEE Press, 2011,: 179-184.
    [9]
    FU H G, PENG J. Improved collaborative filtering algorithm based on model users [J]. Computer Engineering, 2011, 37(3):70-71,74.
    [10]
    WEI S Y, YE N, ZHANG S, et al. Collaborative filtering recommendation algorithm based on item clustering and global similarity[C]// Proceedings of the 5th International Conference on Business Intelligence and Financial Engineering. Lanzhou, China: ACM Press, 2012: 69-72.
    [11]
    SALAKHUTDINOV R, MNIH A, HINTON G. Restricted Boltzmann machines for collaborative filtering[C]// Proceedings of the 24th International Conference on Machine Learning. Corvallis, USA: ACM Press, 2007: 791-798.
    [12]
    STRUNJAS S. Algorithms and models for collaborative filtering from large information corpora[D]. University of Cincinnati, USA, 2008.
    [13]
    SHINDE S K, KULKARNI U V. Hybrid personalized recommender system using fast K-medoids clustering algorithm [J]. Journal of Advances in Information Technology, 2011, 2(3): 152-158.
    [14]
    JIANG J, LU J, ZHANG G Q, et al. Scaling-up item-based collaborative filtering recommendation algorithm based on hadoop[J]. Computer Science Technology, 2011, 7(4): 123-126.)
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    [1]
    ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Transaction on Knowledge and Data Engineering, 2005, 17(6):734-749.
    [2]
    刘青文. 基于协同过滤的推荐算法研究[D]. 中国科学技术大学, 2013.
    [3]
    MENG X W, HU X, WANG L C, et al. Mobile recommender systems and their applications[J]. Journal of Software, 2013, 24(1):91-108.
    [4]
    CONSTANTINOPOULOS C, LIKAS A. Unsupervised learning of Gaussian mixtures based on variational component splitting[J]. IEEE Transactions on Neural Networks, 2007, 18(3): 745-755.
    [5]
    CACHEDA F, CARNEIRO V, FERNNDEZ D, et al. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems[J]. ACM Transactions on the Web, 2011, 5(1): 161-171.
    [6]
    NEWTON J, GREINER R. Hierarchical probabilistic relational models for collaborative filtering[C]// Proceedings of the 21st International Conference on Machine Learning—Workshop on Statistical Relational Learning. New York: ACM Press, 2004: 249-163.
    [7]
    BELL R, KOREN Y, VOLINSKY C. Modeling relationships at multiple scales to improve accuracy of large recommender systems[C]// Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose: ACM Press, 2007: 95-104.
    [8]
    DAKHEL G M, MAHDAVI M. A new collaborative filtering algorithm using K-means clustering and neighbors’ voting[C]// Proceedings of the 11th International Conference on Hybrid Intelligent Systems. Melacca, Malaysia: IEEE Press, 2011,: 179-184.
    [9]
    FU H G, PENG J. Improved collaborative filtering algorithm based on model users [J]. Computer Engineering, 2011, 37(3):70-71,74.
    [10]
    WEI S Y, YE N, ZHANG S, et al. Collaborative filtering recommendation algorithm based on item clustering and global similarity[C]// Proceedings of the 5th International Conference on Business Intelligence and Financial Engineering. Lanzhou, China: ACM Press, 2012: 69-72.
    [11]
    SALAKHUTDINOV R, MNIH A, HINTON G. Restricted Boltzmann machines for collaborative filtering[C]// Proceedings of the 24th International Conference on Machine Learning. Corvallis, USA: ACM Press, 2007: 791-798.
    [12]
    STRUNJAS S. Algorithms and models for collaborative filtering from large information corpora[D]. University of Cincinnati, USA, 2008.
    [13]
    SHINDE S K, KULKARNI U V. Hybrid personalized recommender system using fast K-medoids clustering algorithm [J]. Journal of Advances in Information Technology, 2011, 2(3): 152-158.
    [14]
    JIANG J, LU J, ZHANG G Q, et al. Scaling-up item-based collaborative filtering recommendation algorithm based on hadoop[J]. Computer Science Technology, 2011, 7(4): 123-126.)
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