DOI QR코드

DOI QR Code

Hybrid Intelligent Web Recommendation Systems Based on Web Data Mining and Case-Based Reasoning

  • Kim, Jin-Sung (School of Business Administration, Jeonju University)
  • Published : 2003.06.01

Abstract

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers' preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can't present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

References

  1. Aamodt, A and Plaza, K, "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches," Artificial Intelligence Communications, IOS Press, 7(11), pp. 39-59, 1994.
  2. Agrawal, R, Imielinski, T., and Swami, A., "Database Mining: A Performance Perspective," IEEE Transactions on Knowledge and Data Engineering, 5(6), pp. 914-925, 1993. https://doi.org/10.1109/69.250074
  3. Aha, D.W., "Case-Based Learning Algorithm," Proceedings of the Case-Based Reasoning Workshop, pp. 147-158, May 1991.
  4. Bonchi, F., Giannotti, F., Gozzi, C., Manco, G., Nanni, M., Pedreschi, D., Renso, C., and Ruggieri, S., "Web Log Data Warehousing and Mining for Intelligent Web Caching," Data & Knowledge Engineering, 39, pp. 165-189, 2001. https://doi.org/10.1016/S0169-023X(01)00038-6
  5. Changchien, S.W., and Lu, T.C., "Mining Association Rule Procedure to Support On-Line Recommendation by Customers and Products Fragmentation," Expert Systems with Applications, 20, pp. 325-335, 2001. https://doi.org/10.1016/S0957-4174(01)00017-3
  6. Chiu, C., "A Case-Based Customer Classification Approach for Direct Marketing," Expert Systems with Applications, 22(2), pp. 163-168, 2002. https://doi.org/10.1016/S0957-4174(01)00052-5
  7. Cho, Y.H., Kim, J.K, and Kim, S.H., "A Personalized Recommender System based on Web Usage Mining and Decision Tree Induction," Expert Systems with Applications, 23(3), pp. 329-342, 2002. https://doi.org/10.1016/S0957-4174(02)00052-0
  8. Choy, K.L., Lee, W.B., and Lo, V., "Development of a Case Based Intelligent Customer-Supplier Relationship Management System," Expert Systems with Applications, 23(3), pp. 281-297, 2002. https://doi.org/10.1016/S0957-4174(02)00048-9
  9. Finnie, G., Sun, Z., "Similarity and Metrics in Case-Based Reasoning," International Journal of Intelligent Systems, 17, pp. 273-287, 2002. https://doi.org/10.1002/int.10021
  10. Fyfe, C., and Corchado, J.M., "Automating the Construction of CBR Systems using Kernel Methods," International Journal of Intelligent Systems, 16, pp. 571-586, 2001. https://doi.org/10.1002/int.1024
  11. Hui, S.C. and Jha, G.. , "Data Mining for Customer Service Support," Information & Management, 38, pp. 1-13, 2000. https://doi.org/10.1016/S0378-7206(00)00051-3
  12. Kannan, P.K and Rao, H.R, "Introduction to the Special Issue: Decision Support Issues in Customer Relationship Management and Interactive Marketing for e-Commerce," Decision Support Systems, 32(2), pp. 83-84, 2001. https://doi.org/10.1016/S0167-9236(01)00103-8
  13. Kim, E., Kim, W., and Lee, Y., "Combination of Multiple Classifiers for the Customer's Purchase Behavior Prediction," Decision Support Systems, 34(2), pp. 167-175, 2002. https://doi.org/10.1016/S0167-9236(02)00079-9
  14. Lee, K.C., Kim, J.S., Chung, N.H., and Kwon, S.J., "Fuzzy Cognitive Map Approach to Web-mining Inference Amplification," Expert Systems with Applications, 22, pp. 197-211, 2002. https://doi.org/10.1016/S0957-4174(01)00054-9
  15. Schirmer, A, "Case-Based Reasoning and Improved Adaptive Search for Project Scheduling," Naval Research Logistics, 47, pp. 201-222, 2000. https://doi.org/10.1002/(SICI)1520-6750(200004)47:3<201::AID-NAV2>3.0.CO;2-L
  16. Song, H.S., Kim, J.K, and Kim, S.H., Mining the Change of Customer Behavior in an Internet Shopping Mall, Expert Systems with Applications, 21, pp. 157-168, 2001. https://doi.org/10.1016/S0957-4174(01)00037-9