Building personalized recommendation system in E-Commerce using association rule-based mining and classification

Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

Volumn 7, Issue , 2007, Pages 4113-4118

  Zhang, Xi Zheng ^a,b  

^a HUNAN INSTITUTE OF ENGINEERING   (China)

^b HUNAN UNIVERSITY   (China)

Author keywords

Association rule mining; Classification E Commerce; Personalized recommendation system

Indexed keywords

ASSOCIATION RULES; CLASSIFICATION (OF INFORMATION); DATA MINING; INTERNET; KNOWLEDGE BASED SYSTEMS;

ASSOCIATION RULE MINING; CLASSIFICATION E-COMMERCE; PERSONALIZED RECOMMENDATION SYSTEMS;

LEARNING SYSTEMS;

EID: 38049019036     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2007.4370866     Document Type: Conference Paper

References (15)

1. Liu Guo-rong , Zhang Xi-zheng. Collaborative Filtering Based Recommendation system for Product bundling. Proceeding of ICMSE'06, Oct., 251-254, Lille, France.
2. Chen, H. C., Chen, A. L. P. A music recommen -ation system based on music data grouping and user interests. Proceedings of the ACM /CIKM.231 -23 8,Atlanta, USA.
3. Francisco, G. S., Rafael. An integrated approach for developing e-commerce applications. Expert Systems with Applications, 2005,28:223-235.
4. Ghani, R., & Fano. Building recommender systems using a knowledge base of product semantics. Proceedings of workshop on recommendation and personalization in ecommerce, Malaga, Spain.
5. Han, P., Xie, B., Yang, F., & Shen, R.. A scalable P2P recommender system based on distributed colla -borative filtering. Expert Systems with Applications, 2006,27: 203-210.
6. Hofmann, T. The cluster-abstraction model: Un -supervised learning of topic hierarchies from text data. Proceedings of 16th international joint conference on artificial intelligence, 682-687,Stockholm, Sweden.
7. Hull, D. A. Improving text retrieval for the routing problem using latent semantic indexing. Proceedings of 17th ACM international conference on research and development in information retrieval, 1999, 282-289, Dublin, Ireland.
8. Lee, W.P., Yang, T.-H. Personalizing information appliances: A multi-agent framework for TV program me recommendations. Expert Systems with Applications, 2003,25:331-341.
9. Liu, B., Hsu, W., & Ma, Y. Integrating classifi -cation and association rule mining. Proceedings of 4th international conference on knowledge discovery and data mining, New York, USA, 1998, 27-31.
10. Mooney, R. J., & Roy, L. Content-based book recommending using learning for text categorization. Proceedings of the ACM international conference on digital libraries, San Antonia, Texas,USA,2000, 195 -204.
11. Schafer, J. B., Konstan, J. Recommender systems in Ecommerce. Proceedings of 1st ACM conference on electronic commerce, Denver, Colorado, USA, 1999, 158-166.
12. R., Antonie, M.-L., & Coman, A. Mammography classification by an association rule-based classifier. International workshop on multimedia data mining, Edmonton, Alberta, Canada, 2002, 62-69.
13. Wang, F.-H., & Shaob, H.-M. Effective personalized recommendation based on time-framed navigation clustering and association mining. Expert Systems with Applications, 2002,27:365-377.
14. Weng, S.-S., & Liu, C.-K. Using text classifi -cation and multiple concepts to answer e-mails. Expert Systems with Applications,2004, 26:529-543.
15. Agrawal, R., Srikant, R. Fast algorithms for mining association rules in large databases. Proceed -ings of 20th international conference on very large data bases, Santiago de Chile, Chile ,2003,487-499.