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Research on Automatic Recommender System Based on Data Mining

  • Qingzhang Chen
  • Qiaoyan Chen
  • Kai Wang
  • Zhongzhe Tang
  • Yujie Pei
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 238)

Abstract

By using ART neural network and data mining technology, this study builds a typical online recommendation system. It can automatically cluster population characteristics and dig out the associated characteristics. Aiming at the characteristics of recommendation system and users’ attribute weights, this paper propose a modified ART algorithm for clustering MART algorithm. It makes recommendation system to set the weight value of each attribute node based on the importance of user attributes. The experiment shows that the MART algorithm has better performance than the conventional ART algorithm and can get more reasonable and flexible clustering results.

Keywords

the automatic recommender system Adaptive Resonance Theory data mining technology association rules 

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References

  1. 1.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-commerce. In: ACM Conference on Electronic Commerce, pp. 158–167 (2000)Google Scholar
  2. 2.
    Yu, P.S.: Data Mining and Personalization Technologies. In: The 6th International Conference on Database Systems for Advanced Applications, pp. 6–13 (1999)Google Scholar
  3. 3.
  4. 4.
    Amazon (2002), http://www.amazon.com
  5. 5.
    Hill, W.C., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proceedings of CHI 1995, pp. 194–201 (1995)Google Scholar
  6. 6.
    Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: GroupLens Applying Collaborative Filtering to Usenet News. Communications of ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  7. 7.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating ’Word of Mouth. In: Proceedings of the Computer-Human Interaction Conference, CHI 1995 (1995)Google Scholar
  8. 8.
  9. 9.
    Massey, L.: On the quality of ART1 text clustering. Neural Networks, 771–778 (2003)Google Scholar
  10. 10.
    Gour, B., Bandopadhyaya, T.K., Sharma, S.: High Quality Cluster Generation of Feature Points of Fingerprint Using Neutral Network. EuroJournals Publishing, 13–18 (2009)Google Scholar
  11. 11.
    Bailin: Research on intrusion detection system based on neural computing and Evolution Network. Xi’an University of Electronic Science and Technology (2005) (Chinese)Google Scholar
  12. 12.
    Bai Y.-l., Li C.-T.: Design of characteristics in patients with cluster model. Chinese General Medical (2007) (Chinese)Google Scholar
  13. 13.
    Zheng, L.-y.: Trie-based algorithm of mining association rules. Journal of Lanzhou University of Technology 30(5), 90–92 (2004) (Chinese) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Qingzhang Chen
    • 1
  • Qiaoyan Chen
    • 1
  • Kai Wang
    • 1
  • Zhongzhe Tang
    • 1
  • Yujie Pei
    • 1
  1. 1.Department of computer science and technologyZhejiang University of technologyHangZhouChina

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