JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 35 Issue 4
July 2020
Article Contents
GUO Jinchao and YANG Jigang. Collaborative filtering algorithm based on the improved SVD algorithm and binary K-means clustering algorithm[J]. Journal of Light Industry, 2020, 35(4): 88-95. doi: 10.12187/2020.04.012
Citation: GUO Jinchao and YANG Jigang. Collaborative filtering algorithm based on the improved SVD algorithm and binary K-means clustering algorithm[J]. Journal of Light Industry, 2020, 35(4): 88-95. doi: 10.12187/2020.04.012 shu

Collaborative filtering algorithm based on the improved SVD algorithm and binary K-means clustering algorithm

  • Received Date: 2020-04-11
  • Aiming at the problem of large sparseness and poor scalability of traditional collaborative filtering algorithms,a collaborative filtering algorithm based on the improved singular value decomposition (SVD) algorithm and binary K-means clustering algorithm was proposed.The algorithm firstly used the improved SVD algorithm to reduce the dimensionality of the sparse user-item rating matrix to obtain the user implicit feature matrix,then used the binary K-means clustering algorithm to cluster similar users to improve the scalability of the algorithm,and finally used the nearest neighbor set score to correct the target user's score to make up for the error caused by the loss of user information due to matrix factorization.Experimental results on the MovieLens 100K data set showed that compared with the traditional user-based collaborative filtering algorithm,K-means clustering-based collaborative filtering algorithm and latent factor model (LFM) algorithm,this method could effectively improve the accuracy of recommendation results.
  • 加载中
    1. [1]

      SUN P,LI Z,HAN Z,et al.An overview of collaborative filtering recommendation algorithm[J].Advanced Materials Research,2013,756-759:3899.

    2. [2]

      张琳,闫强.基于管理和消费者行为视角的个性化推荐研究与展望[J].北京邮电大学学报(社会科学版),2016,18(6):24.

    3. [3]

      BRUSILOVSKY P,KOBSA A,NEJDL W.The adaptive web:Methods and strategies of web personalization[J].Lecture Notes in Computer Science,2007,2002(5):377.

    4. [4]

      LI B,ZHU X Q,LI R J,et al.Cross-domain collaborative filtering over time[C]//Proceedings of the 22nd International Joint Conference on Artificial Intelligence.Palo Alto:AAAI Press,2011:2293.

    5. [5]

      SUGANESHWAR G,IBRAHIM S P.A survey on collaborative filtering based recommendation system[C]//3rd International Symposium on Big Data and Cloud Computing.Chengdu:Springer-Verlag,2016:503.

    6. [6]

      KOOHI H,KIANI K.User based collaborative filtering using fuzzy C-means[J].Measurement,2016,91(1):134.

    7. [7]

      SU X,KHOSHGOFTAAR T M.Collaborative filtering for multi-class data using belief nets algorithms[J].International Journal on Artificial Intelligence Tools,2008,17(1):71.

    8. [8]

      孙小华,陈洪,孔繁胜.在协同过滤中结合奇异值分解与最近邻方法[J].计算机应用研究,2006,23(9):206.

    9. [9]

      刘艺,冯钧,魏童童,等.一种改进的协同过滤推荐算法[J].计算机与现代化,2017(1):1.

    10. [10]

      SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web.New York:ACM,2001:285.

    11. [11]

      PANDEY A,PANDEY R.Elective recommendation support through K-means clustering using R-tool[C]//International Conference on Computational Intelligence and Communication Networks.Kolkata:ICRCICN,2016:851.

    12. [12]

      HARPER F M,KONSTAN J A.The MovieLens datasets:History and context[J].ACM Transactions on Interactive Intelligent Systems,2015,5(4):1.

    13. [13]

      陈清洁.基于SVD的协同过滤推荐算法研究[D].成都:西南交通大学,2017.

Article Metrics

Article views(1260) PDF downloads(9) Cited by()

Ralated
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return