Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Collaborative CRM using Statistical Learning Theory and Bayesian Fuzzy Clustering

  • Published : 2004.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

According to the increase of internet application, the marketing process as well as the research and survey, the education process, and administration of government are very depended on web bases. All kinds of goods and sales which are traded on the internet shopping malls are extremely increased. So, the necessity of automatically intelligent information system is shown, this system manages web site connected users for effective marketing. For the recommendation system which can offer a fit information from numerous web contents to user, we propose an automatic recommendation system which furnish necessary information to connected web user using statistical learning theory and bayesian fuzzy clustering. This system is called collaborative CRM in this paper. The performance of proposed system is compared with the other methods using real data of the existent shopping mall site. This paper shows that the predictive accuracy of the proposed system is improved by comparison with others.

Keywords

References

  1. 퍼지 및 지능시스템학회 논문지 v.13 no.1 붓스트랩 기법과 유전자 알고리즘을 이용한 최적 군집수 결정 박민재;전성해;오경환
  2. 한국통계학회논문집 v.19 no.1 Web Log Analysis using Support Vector Regression 전성해;임민택;전홍석;황진수;최성용;김지연;오경환 https://doi.org/10.5351/CKSS.2003.10.1.061
  3. 한국정보과학회 2003 춘계학술대회 발표논문집(Ⅱ) 군집화를 위한 베이지안 학습기반의 퍼지규칙 추출 한진우;전성해;오경환
  4. Proceedings of the Workshop on Recommendation system Recommendation as classification: Using Social and Content-based Information in Recommendation Basu,C.;Hirsh,H.;Cohen,W.
  5. Pattern Recognition with Fuzzy Objective Function Algorithms Bezdek,J.C.
  6. Neural Networks for Pattern Recognition Bishop,C.M.
  7. Machine Learning v.20 Support Vector Networks Cortes,C.;Vapnik,V.N.
  8. SIGIR SWAMI: A Framework for Collaborative Filtering Algorithm Development and Evaluation Fisher,D.;Hildrum,K.;Hong,J.;Newman,M.;Thomas,M.;Vuduc,R.
  9. Forrester Research
  10. Bayesian Data Analysis Gelman,A.;Carlin,J.B.;Stern,H.S.;Rubin,D.B.
  11. IEEE Trans v.1 no.3 Switching Regression Models and Fuzzy Clustering Hathaway,R.J.;Bezdek,J.C. https://doi.org/10.1109/91.236552
  12. KDD Cup
  13. In proceeding 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics Nonlinear programming Kuhn,H.W.;Tucker,A.W.
  14. IEEE Internet Computing v.7 no.Issue 1 Amazon.com recommendations: item-to-item collaborative filtering Linden,G.;Smith,B.;York,J. https://doi.org/10.1109/MIC.2003.1167344
  15. Bayesian Statistics 7 Bayesian Clustering with Variable and Transformation Selections Liu,J.S.;Zhang,J.L.;Palumbo,M.L.;Lawrence,C.E.
  16. ACM conference on Computer Supported Cooperative Work GroupLens: An Open Architecture for Collaborative Filtering of Netnews Paul,R.;Neophytos,L.;Mitesh,S.;Peter,B.;John,R.
  17. MIT AI Memo no.1612 Properties of support vector machine Pontil,M.;Verri,A.
  18. Bayesian Statistics: Principles, Models, and Applications Press,S.J.
  19. Monte Carlo Statistical Methods Robert,C.P.;Casella,G.
  20. Master's thesis Regression estimation with support vector learning machines Smola,A.J.
  21. UCI Machine Learning Repository
  22. The Nature of Statistical Learning Theory Vapnik,V.N.
  23. Statistical Learning Theory Vapnik,V.N.
  24. Fuzzy Set Theory and Its Application Zimmermann,H.J.