ARTMAP-based data mining approach and its application to library book recommendation

2009 International Symposium on Intelligent Ubiquitous Computing and Education, IUCE 2009

Volumn , Issue , 2009, Pages 26-29

  Yang, Xuejun ^a   Zeng, Hongchun ^a   Huang, Yonghong ^a  

^a CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

ARTMAP; Data mining; Database; Library

Indexed keywords

ART2 NETWORK; ARTMAP; ARTMAP NETWORK; C++ LANGUAGE; COMPUTER PROGRAM; CONVENTIONAL DATA MINING; DATA MINING SYSTEM; DATABASE; MODEL-BASED; NEIGHBORHOOD ALGORITHM; RECOMMENDATION SYSTEMS; REFERENCE VECTORS; SIMULATION EXPERIMENTS; UNIVERSITY LIBRARIES; USER PROFILE;

ALGORITHMS; COMPUTER SOFTWARE; INTELLIGENT COMPUTING; MINING; SIMULATORS; UBIQUITOUS COMPUTING;

DATA MINING;

EID: 70350707729     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IUCE.2009.43     Document Type: Conference Paper

References (13)

1. G. Carpenter, S. Grossberg and J. H. Reynolds, "ARTMAP: Supervised Real-Time Learning and Classification of Non-stationary Data by A Self-Organizing Neural Network", Neural Networks, vol. 19, 2006, pp. 565-588.
2. P. Resnick, P. N. Iacovou and J. Riedl, "Grouplens: An Open Architecture for Collaborative Filtering of Netnews", Proceedings of the ACM Conference on Computer Supported Cooperative Work", Oct. 2005, pp. 175-186.
3. S. Ansari and R. Kohli, "Internet Recommendation Systems", Journal of Marketing Research, vol.37(3), 2002, pp.34-67.
4. R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rules Between Sets of Items in Large Databases", Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Washington DC, 2003, pp. 207-216.
5. C. Aggarwal, Z. Sun and P. S. Yu, "Online Algorithms for Finding Profile Association Rules", Proc. of 5th Int'l. Conf. on Information and Knowledge Management (CIKM98), Sept.2006, pp. 86-95.
6. J. Han, Y. Cai and N. Cercone, "Data-Driven Discovery of Quantitative Rules in Relational Databases", IEEE Trans. Knowledge and Data Engineering, vol. 5, 2003, pp. 29-40.
7. S. Y. Hwang and E. P. Lim, "A Data Mining Approach to Library New Book Recommendations", Proc. of the Int'l. Conference on Asia Digital Libraries,May 2005,pp.56-60.
8. J. W. Kim, B. H. Lee and M. Nelson, "Application of Decision-tree Induction Techniques to Personalized Advertisements on Internet Storefronts", International Journal of Electronic Commerce, vol. 5(3), 2006, pp. 45-62.
9. W. Lin, S. A. Alvarez, and C. Rujz, "Collaborative Recommendation via Adaptive Association Rule Mining", Proc. of WebKDD Workshop, Aug. 2003,pp. 34-38.
10. R. J. Mooney and L. Roy, "Content-Based Book Recommending Using Learning for Text Categorization", Proc. of the 5'th ACM Conf. on Digital Libraries, Jun. 2005, pp.195-240.
11. M. J. Pazzani, "A Framework for Collaborative, Content-Based and Demographic Filtering", Artificial Intelligence Review, vol. 14, 2004, pp.393-408.
12. G. Padmanabhan and A. Tuzhilin, "Small is Beautiful: Discovering The Minimal Set of Unexpected Patterns", Proc. of the 6'th ACM SIGKDD Int'l. Conf. on Knowledge Discovery and Data Mining, IEEE Press, Aug. 2003, pp.54-63.
13. J. B. Schafer, J. A. Konstan and J. Riedl, "E-Commerce Recommendation Applications", Data Mining and Knowledge Discovery, vol. 5(1), 2007, pp. 115-153.