Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce

Expert Systems with Applications

Volumn 26, Issue 2, 2004, Pages 233-246

  Cho, Yoon Ho ^a   Kim, Jae Kyeong ^b  

^a Dongyang University   (South Korea)

^b Kyung Hee University   (South Korea)

Author keywords

Collaborative filtering; Internet marketing; Personalized recommendation; Product taxonomy; Web usage mining

Indexed keywords

DATABASE SYSTEMS; ELECTRONIC COMMERCE; INFORMATION USE; MARKETING; WORLD WIDE WEB;

COLLABORATIVE FILTERING; TAXONOMY; WEB USAGE MINING;

EXPERT SYSTEMS;

EID: 0346961206     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0957-4174(03)00138-6     Document Type: Article

References (42)

1. Adomavicius G., Tuzhilin A. Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery. 5:(1-2):2001;33-58.
2. Aggarwal C.C., Yu P.S. Data mining techniques for personalization. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering. 23:2000;4-9.
3. Balabanović M., Shoham Y. Fab: Content-based, collaborative recommendation. Communications of the ACM. 40:(3):1997;66-72.
4. Basu C., Hirsh H., Cohen W. Recommendation as classification: Using social and content-based information in recommendation. Proceedings of the 15th National Conference on Artificial Intelligence. 1998;714-720.
5. Berry J.A., Linoff G. Data mining techniques: For marketing, sales, and customer support. 1997;Wiley, New York.
6. Billsus D., Pazzani M.J. Learning collaborative information filters. Proceedings of the 15th International Conference on Machine Learning. 1998;46-54.
7. Breese J.S., Heckerman D., Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 1998;43-52.
8. Cho Y.H., Kim J.K., Kim S.H. A personalized recommender system based on Web usage mining and decision tree induction. Expert Systems with Applications. 23:(3):2002;329-342.
9. Claypool M., Gokhale A., Miranda T., Murnikov P., Netes D., Sartin M. Combining content-based and collaborative filters in an online newspaper. Proceedings of the ACM SIGIR'99 Workshop on Recommender Systems, Berkeley, CA. August:1999.
10. Condliff M., Lewis D.D., Madigan D., Posse C. Bayesian mixed-effects models for recommender systems. Proceedings of the ACM SIGIR'99 Workshop on Recommender Systems, Berkeley, CA. August:1999.
11. Cooley R., Mobasher B., Srivastava J. Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems. 1:1999;5-32.
12. Good N., Schafer J.B., Konstan J., Borchers A., Sarwar B., Herlocker J., Riedl J. Combining collaborative filtering with personal agents for better recommendations. Proceedings of the AAAI'99. 1999;439-446.
13. Han J., Fu Y. Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. Proceedings of the AAAI'94 Workshop on Knowledge Discovery in Databases. 1994;157-168.
14. Han J., Fu Y. Discovery of multiple-level association rules from large databases. Proceedings of the International Conference on Very Large Data Bases. 1995;420-431.
15. Han J., Fu Y. Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering. 11:(5):1999;798-804.
16. Herlocker J., Konstan J., Borchers A., Riedl J. An algorithmic framework for performing collaborative filtering. Proceedings of the Conference on Research and Development in Information Retrieval. 1999;230-237.
17. Hill W., Stead L., Rosenstein M., Furnas G. Recommending and evaluating choices in a virtual community of use. Proceedings of the ACM Conference on Human Factors in Computing Systems. 1995;194-201.
18. Hoffman T., Puzicha J. Latent class models for collaborative filtering. Proceedings of the 16th International Joint Conference on Artificial Intelligence. 1999;688-693.
19. Kim J.K., Cho Y.H., Kim W.J., Kim J.R., Suh J.Y. A personalized recommendation procedure for Internet shopping support. Electronic Commerce Research and Applications. 1:(3):2002;301-313.
20. Konstan J.A., Miller B.N., Maltz D., Herlocker J.L., Gordon L.R., Riedl J. GroupLens: Applying collaborative filtering to usenet news. Communications of the ACM. 40:(3):1997;77-87.
21. Lawrence R.D., Almasi G.S., Kotlyar V., Viveros M.S., Duri S.S. Personalization of supermarket product recommendations. Data Mining and Knowledge Discovery. 5:(1-2):2001;11-32.
22. Lee J., Podlaseck M., Schonberg E., Hoch R. Visualization and analysis of clickstream data of online stores for understanding Web merchandising. Data Mining and Knowledge Discovery. 5:(1-2):2001;59-84.
23. Lin W., Alvarez S.A., Ruiz C. Collaborative recommendation via adaptive association rule mining. Proceedings of the WEBKDD. 2000.
24. Lin W., Alvarez S.A., Ruiz C. Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery. 6:2002;83-105.
25. Melville P., Mooney R.J., Nagarajan R. Content-boosted collaborative filtering. ACM SIGIR Workshop on Recommender Systems. 2001.
26. Menascé D.A., Almeida V.A., Fonseca R., Mendes M.A. A methodology for workload characterization of e-commerce sites. Proceedings of the ACM E-Commerce. 1999;119-128.
27. Mobasher B., Cooley R., Srivastava J. Automatic personalization based on Web usage mining. Communications of the ACM. 43:(8):2000;142-151.
28. Mobasher B., Dai H., Luo T., Sun Y., Zhu J. Integrating Web usage and content mining for more effective personalization. Proceedings of the EC-Web 2000. 2000;165-176.
29. Resnick P., Iacovou N., Suchak M., Bergstrom P., Riedl J. GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work. 1994:1994;175-186.
30. Rijsbergen C.J. Information retrieval. 2nd ed. 1979;Butterworths, London.
31. Rucker J., Polano M.J. Siteseer: Personalized navigation for the Web. Communications of the ACM. 40:(3):1997;73-75.
32. Sarwar, B (2001). Sparsity, scalability, and distribution in recommender systems. PhD thesis, University of Minnesota.
33. Sarwar B., Karypis G., Konstan J.A., Riedl J. Application of dimensionality reduction in recommender system - A case study. Proceedings of the ACM WebKDD-2000 Workshop. 2000.
34. Sarwar B., Karypis G., Konstan J.A., Riedl J. Analysis of recommendation algorithms for e-commerce. Proceedings of the ACM E-Commerce. 2000;158-167.
35. Sarwar B., Karypis G., Konstan J.A., Riedl J. Item-based collaborative filtering recommendation algorithm. Proceedings of the 10th International World Wide Web Conference. 2001;285-295.
36. Sarwar B., Konstan J., Borchers A., Herlocker J., Miller B., Riedl J. Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. Proceedings of the CSCW'98. 1998;345-354.
37. Shardanand U., Maes P. Social information filtering: Algorithms for automating 'word of mouth'. Proceedings of the Conference on Human factors in Computing Systems. 1995;210-217.
38. Soboroff I.M., Nicholas C.K. Combining content and collaboration in text filtering. Proceedings of the IJCA'99 Workshop on Machine Learning in Information Filtering. '99:1999;86-91.
39. Srivastava J., Cooley R., Deshpande M., Tan P. Web usage mining: Discovery and applications of usage patterns from Web data. SIGKDD Explorations. 1:(2):2000;1-12.
40. Terveen L., Hill W., Amento B., McDonald D.W., Creter J. PHOAKS: A system for sharing recommendations. Communications of the ACM. 40:(3):1997;59-62.
41. Ungar L.H., Foster D.P. A formal statistical approach to collaborative filtering. Proceedings of the Conference on Automated Learning and Discovery. 1998.
42. VanderMeer D., Dutta K., Datta A. Enabling scalable online personalization on the Web. Proceedings of the ACM E-Commerce. 2000;185-196.