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Volume 27 Issue 3
May  2012
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Kiatichai Treerattanapitak, Chuleerat Jaruskulchai. Exponential Fuzzy C-Means for Collaborative Filtering[J]. Journal of Computer Science and Technology, 2012, 27(3): 567-576. DOI: 10.1007/s11390-012-1244-x
Citation: Kiatichai Treerattanapitak, Chuleerat Jaruskulchai. Exponential Fuzzy C-Means for Collaborative Filtering[J]. Journal of Computer Science and Technology, 2012, 27(3): 567-576. DOI: 10.1007/s11390-012-1244-x
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Exponential Fuzzy C-Means for Collaborative Filtering

  • Department of Computer Science, Kasetsart University, 50 Ngamwongwan Rd., Jatuchak, Bangkok, Thailand

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  • Author Bio:

    Kiatichai Treerattanapitak is currently a Ph.D. candidate in Kasetsart University, Thailand. He is also a senior consultant of envi-ronmental resources management in Thailand. His research interests in-clude artificial inteligence, data min-ing, and clustering.

  • Received Date: August 31, 2011
  • Revised Date: February 29, 2012
  • Published Date: May 04, 2012
    Abstract
  • Collaborative filtering (CF) is one of the most popular techniques behind the success of recommendation system. It predicts the interest of users by collecting information from past users who have the same opinions. The most popular approaches used in CF research area are Matrix factorization methods such as SVD. However, many well-known recommendation systems do not use this method but still stick with Neighborhood models because of simplicity and explainability. There are some concerns that limit neighborhood models to achieve higher prediction accuracy. To address these concerns, we propose a new exponential fuzzy clustering (XFCM) algorithm by reformulating the clustering's objective function with an exponential equation in order to improve the method for membership assignment. The proposed method assigns data to the clusters by aggressively excluding irrelevant data, which is better than other fuzzy C-means (FCM) variants. The experiments show that XFCM-based CF improved 6.9% over item-based method and 3.0% over SVD in terms of mean absolute error for 100K and 1M MovieLens dataset.
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    • References (29)
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