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Using Web Usage Mining and SVD to Improve E-commerce Recommendation Quality

  • Jae Kyeong Kim
  • Yoon Ho Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2891)

Abstract

Collaborative filtering is the most successful recommendation method, but its widespread use has exposed some well-known limitations, such as sparsity and scalability. This paper proposes a recommendation methodology based on Web usage mining and SVD (Singular Value Decomposition) to enhance the recommendation quality and the system performance of current collaborative filtering-based recommender systems. Web usage mining populates the rating database by tracking customers’ shopping behaviors on the Web, so leading to better quality recommendations. SVD is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. Several experiments on real Web retailer data show that the proposed methodology provides higher quality recommendations and better performance than other recommendation methodologies.

Keywords

Recommender System Collaborative Filter Shopping Behavior Recommendation Methodology Recommendation List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jae Kyeong Kim
    • 1
  • Yoon Ho Cho
    • 2
  1. 1.School of Business AdministrationKyungHee UniversitySeoulKorea
  2. 2.Department of Internet InformationDongyang Technical CollegeSeoulKorea

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