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)


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.


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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  1. 1.
    Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proc. 15th International Conference on Machine Learning, pp. 46–54 (1998)Google Scholar
  2. 2.
    Cho, Y.H., Kim, J.K., Kim, S.H.: A personalized recommender system based on Web usage mining and decision tree induction. Expert Systems with Applications 23, 329–342 (2002)CrossRefGoogle Scholar
  3. 3.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Proc. ACM SIGIR 1999 Workshop on Recommender Systems, Berkeley, CA (1999)Google Scholar
  4. 4.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems 1, 5–32 (1999)Google Scholar
  5. 5.
    Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proc. Conference on Research and Development in Information Retrieval, pp. 230–237 (1999)Google Scholar
  6. 6.
    Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proc. ACM Conference on Human Factors in Computing Systems, pp. 194–201Google Scholar
  7. 7.
    Kanth, K.V., Agrawal, D., Abbadi, A.E., Singh, A.: Dimensionality Reduction for Similarity Searching in Dynamic Databases. Computer Vision and Image Understanding 75, 59–72 (1999)CrossRefGoogle Scholar
  8. 8.
    Kim, J.K., Cho, Y.H., Kim, W.J., Kim, J.R., Suh, J.Y.: A personalized recommendation procedure for Internet shopping support. Electronic Commerce Research and Applications 1, 301–313 (2002)CrossRefGoogle Scholar
  9. 9.
    Lee, J., Podlaseck, M., Schonberg, E., Hoch, R.: Visualization and analysis of clickstream data of online stores for understanding Web merchandising. Data Mining and Knowledge Discovery 5, 59–84 (2001)CrossRefGoogle Scholar
  10. 10.
    Lin, W., Alvarez, S.A., Ruiz, C.: Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery 6, 83–105 (2001)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Menasce, D.A., Almeida, V.A., Fonseca, R., Mendes, M.A.: A methodology for workload characterization of e-commerce sites. In: Proc. ACM E-Commerce, pp. 119–128 (1999)Google Scholar
  12. 12.
    Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on Web usage mining. Communications of the ACM 43, 142–151 (2000)CrossRefGoogle Scholar
  13. 13.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens an open architecture for collaborative filtering of netnews. In: Proc. ACM 1994 Conference on Computer Supported Cooperative Work, pp. 175–186 (1994)Google Scholar
  14. 14.
    Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Application of dimensionality reduction in recommender system – a case study. In: Proc. ACM WebKDD 2000 Workshop (2000)Google Scholar
  15. 15.
    Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proc. ACM E-Commerce, pp. 158–167 (2000)Google Scholar
  16. 16.
    Shardanand, U., Maes, P.: Social information filtering algorithms for automating word of mouth. In: Proc. Conference on Human factors in Computing Systems, pp. 210–217 (2000)Google Scholar
  17. 17.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: discovery and applications of usage patterns fromWeb data. SIGKDD Explorations 1, 1–12 (2000)CrossRefGoogle Scholar
  18. 18.
    VanderMeer, D., Dutta, K., Datta, A.: Enabling scalable online personalization on the Web. In: Proc. ACM E-Commerce, pp. 185–196 (2000)Google Scholar

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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