“A Novel Collaborative Filtering Using Kernel Methods for Recommender Systems,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 609-614, 2012,
Citation: “A Novel Collaborative Filtering Using Kernel Methods for Recommender Systems,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 609-614, 2012,

A Novel Collaborative Filtering Using Kernel Methods for Recommender Systems

  • Received Date: 2011-09-01
  • Rev Recd Date: 2011-11-01
  • Publish Date: 2012-10-25
  • Recommender systems form an essential part of e-business systems. Collaborative filtering (CF), a widely used technique by recommender systems, performs poorly for cold start users and is vulnerable to shilling attacks. Therefore, a novel CF using kernel methods for prediction is proposed. The method is called Iterative kernelbased CF (IKCF), for it is an iterative process. First, mode or mean is used to smooth the unknown ratings; second, discrete or continuous kernel estimators are used to generate predicted ratings iteratively and to export the predicted ratings in the end. The experimental results on three real-world datasets show that, with IKCF as a booster, the prediction accuracy of recommenders can be significantly improved especially for sparse datasets. IKCF can also achieve high prediction accuracy with a small number of iteration.
  • loading
  • J. Riedl, B. Smyth, “Introduction to special issue on recommendersystems”, ACM Transactions on the Web, Vol.5, No.1,Article 1, pp.1-2, 2011.
    F. Cacheda, V. Carneiro, D. Fernandez and V. Formoso, “Comparisonof collaborative filtering algorithms: limitations of currenttechniques and proposals for scalable, high-performancerecommender systems”, ACM Transactions on the Web, Vol.5,No.1, Article 2, pp.1-33, 2011.
    G. Linden, B. Smith and J. York, “Amazon.com recommendations:item-to-item collaborative filtering”, IEEE InternetComputing, Vol.7, No.1, pp.76-80, 2003.
    L.J. Fang, H. Kim, K. LeFevre and A. Tami, “A privacy recommendationwizard for users of social networking sites”, Proceedingsof the 17th ACM Conference on Computer and CommunicationsSecurity, Chicago, IL, USA, pp.630-632, 2010.
    J.J. Wu, H. Xiong and J. Chen, “COG: Local decompositionfor rare class analysis”, Data Mining and Knowledge Discovery,Vol.20, No.2, pp.191-220, 2010.
    Z.A. Wu, J. Cao, B. Mao and Y.Q. Wang, “Semi-SAD: applyingsemi-supervised learning to shilling attack detection”, The5th ACM Conference on Recommender Systems, Chicago, IL,USA, pp.289-292, 2011.
    C.A. Micchelli, M. Pontil, “Learning the kernel function viaregularization”, Journal of Machine Learning Research, Vol.6,pp.1099-1125, 2005.
    J.B. Li, L.J. Yu and S.H. Sun, “Refined kernel principal componentanalysis based feature extraction”, Chinese Journal ofElectronics, Vol.20, No.3, pp.467-470, 2011.
    Y. Koren, “Factor in the neighbors: scalable and accurate collaborativefiltering”, ACM Transactions on Knowledge Discoveryfrom Data, Vol.4, No.1, pp.1-24, 2009.
    X.F. Zhu et al., “Missing value estimation for mixed-attributedata sets”, IEEE Transactions on Knowledge and Data Engineering,Vol.23, No.1, pp.110-121, 2011.
    S.C. Zhang, Z.J. and X.F. Zhu, “Missing data imputation byutilizing information within incomplete instances”, The Journalof Systems and Software, Vol.84, No.3, pp.452-459, 2011.
    Y.S. Qin et al., “POP algorithm: kernel-based imputation totreat missing values in knowledge discovery from databases”,Expert Systems with Applications, Vol.36, No.2, pp.2794-2804,2009.
    E.M. Jordaan, “Development of robust inferential sensors: industrial application of support vector machines for regression”,Ph.D. Thesis, Technical University Eindhoven, 2002.
    R.A. Horn, C.R. Johnson, Matrix Analysis, Cambridge UniversityPress, 1985.
    Jester Joke. http://www.ieor.berkeley.edu/~goldberg/jesterdata,2011.
    GroupLens Research. http://www.grouplens.org/node/73,2011.
    P. Massa and P. Avesani, Trust Metrics in Recommender Systems,Computing with Social Trust: Human-Computer InteractionSeries, Springer, pp.259-285, 2009.
  • 加载中


    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

    Article Metrics

    Article views (396) PDF downloads(1041) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint