An architecture for personalized systems based on web mining agents

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3140, Issue , 2004, Pages 563-567

  Moreno, María N ^a   García, Francisco J ^a   Jose Polo M ^a  

^a UNIVERSITY OF SALAMANCA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

COMPLEX ACTIVITY; INFORMATION ITEMS; INTELLIGENT DATA MININGS; PERSONALIZATIONS; PERSONALIZED RECOMMENDATION; SYSTEM IMPLEMENTATION; WEB MINING; WEB SYSTEM;

DATA MINING;

EID: 33751569217     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-27834-4_68     Document Type: Conference Paper

References (14)

1. Cheung, K.W., Kwok, J.T., Law, M.H. and Tsui, K.C.: Mining customer product ratings for personalized marketing. Decision Support Systems, 35 (2003) 231-243.
2. Cho, H.C., Kim, J.K., Kim, S.H.: A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with App. 23 (2002), 329-342.
3. Konstant, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L. and Riedl, J.: GroupLens: Applying collaborative filtering to usenet news. Comm. of the ACM, 40 (1997), 77-87
4. Lee, CH., Kim, Y.H., Rhee, P.K.: Web personalization expert with combining collaborative filtering and association rule mining technique. Expert Systems with Applications 21 (2001), 131-137.
5. Mineset user’s guide, v. 007-3214-004, 5/98. Silicon Graphics (1998).
6. Mobasher, B., Cooley, R. and Srivastava, J.: Automatic personalization based on web usage mining, Communications of the ACM, 43 (8) (2000), 142-151.
7. Moreno, M.N., Miguel, L.A., García, F.J., Polo, M.J.: Data mining approaches for early software size estimation. Proc. 3rd ACIS International Conference On Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD’02), 361-368, Madrid, Spain (2002).
8. Moreno, M.N., Miguel, L.A., García, F.J., Polo, M.J.: Building knowledge discoverydriven models for decision support in project management. Dec.Support Syst. (in press).
9. Padmanabhan, B., Tuzhilin, A.: Knowledge refinement based on the discovery of unexpected patterns in data mining. Decision Support Systems 27 (1999) 303–318.
10. Padmanabhan, B., Tuzhilin, A.: Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems 33 (2002) 309–321.
11. Resnick, P., Iacovou, N., Suchack, M., Bergstrom, P. and Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. Proc. of ACM CSW’94 Conference on Computer.Supported Cooperative Work, 175-186, (1994).
12. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithm. Proc. of the tenth Int. WWW Conference (2001), 285-295.
13. Schafer, J.B., Konstant, J.A. and Riedl, J.: E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5 (2001), 115-153.
14. Wolf, J., Aggarwal, C., Wu, K.L. and Yu, P.: Horting hatches an egg. A new graphtheoretic approach to collaborative filtering. Proc. of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, C.A., (1999).