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Analysis and design of e-supermarket shopping recommender system

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Published:15 August 2005Publication History

ABSTRACT

In this paper we study the application of recommender systems to E-supermarket. The authors analyze personalized requirement for customer in E-supermarket, and design the model of E-supermarket shopping recommender system. A dynamic hybrid recommendation method is presented.

References

  1. Resnick and Varian. Recommender systems. Communications of the ACM, 40(3):56"C58, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Schafer, J. B., Konstan, J. and Riedl, J.: 1999, 'Recommender Systems in E-Commerce'. In: EC '99: Proceedings of the First ACM Conference on Electronic Commerce, Denver, CO, pp. 158--166. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Pazzani, M. J.: 1999, 'A Framework for Collaborative, Content-Based and Demographic Filtering'. Artificial Intelligence Review, 13 (5/6), 393--408. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Andreas Milda,, Thomas Reutterer, An improved collaborative filtering approach for prediction gcross-category purchases based on binary market basket data, Journal of Retailing and Consumer Services, 10 (2003) 123 -- 133Google ScholarGoogle ScholarCross RefCross Ref
  5. Yu Li, Liu Lu, LI Xuefeng, A Hybrid Collaborative Filtering Method for Multiple-interests and Multiple-content Recommendation in E-Commerce, Expert System with Application, Jan. 2005, Vol.28 P67-P77Google ScholarGoogle Scholar
  6. Yu Li, Liu Lu, Personalized Recommendation in E-Commerce and its Application in China, The Seventh International Conference on Industrial Management, Oct. 2004, JapanGoogle ScholarGoogle Scholar
  7. Yu Li, Liu Lu, LI Xuefeng, (2004) Study on Personalized Recommendation Algorithm for User's Multiple Interests, Vol. 10 No.12, P1610-P1615 (Chinese)Google ScholarGoogle Scholar
  8. Yu Li, Liu Lu, Collaborative Filtering Algorithm Based on Mutual Information, The Eighth Pacific Asia Conference on Information System, Aug. 2004, Shanghai, China, P274-P287Google ScholarGoogle Scholar
  9. Yu Li, Liu Lu, Matrix view of the smallest association rule set based on largest frequent item-set, Computer Engineering and Application, No. 23, 2003 (Chinese)Google ScholarGoogle Scholar
  10. Andreas Mild, Martin Natter, A critical view on recommendation systems, Working Paper Series, Vienna University of Economics and Business Administration, Austria, http://www.wu-wien.ac.at/amGoogle ScholarGoogle Scholar

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  1. Analysis and design of e-supermarket shopping recommender system

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    • Published in

      cover image ACM Other conferences
      ICEC '05: Proceedings of the 7th international conference on Electronic commerce
      August 2005
      957 pages
      ISBN:1595931120
      DOI:10.1145/1089551
      • Conference Chairs:
      • Qi Li,
      • Ting-Peng Liang

      Copyright © 2005 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 15 August 2005

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      Overall Acceptance Rate150of244submissions,61%

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