Intelligent information personalization leveraging constraint satisfaction and association rule methods

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

Volumn 4013 LNAI, Issue , 2006, Pages 134-145

  Abidi, Syed Sibte Raza ^a   Zeng, Yan ^a  

^a DALHOUSIE UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATIVE PROCESSING; CONSTRAINT THEORY; INFORMATION MANAGEMENT; MATHEMATICAL MODELS; OPTIMIZATION; PROGRAM DOCUMENTATION; USER INTERFACES;

ASSOCIATION RULES; CONTENT BASED FILTERING; MULTIPLE DOCUMENTS; RECOMMENDER SYSTEMS;

INTELLIGENT AGENTS;

EID: 33746031425     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11766247_12     Document Type: Conference Paper

References (20)

1. Belkin N.J., Croft W.B. Information personalization and information retrieval: Two sides of the same coin? Communications of the ACM, 35(12), 1992, 29-38.
2. Baeza-Yates, R., Ribeiro-Neto, B. Modern Information Retrieval, Addison-Wesley, 1999.
3. Hanani, U., Shapira, B., Shoval, P. Information Filtering: Overview of Issues, Research and Systems. User Modeling and User-Adapted Interaction, 11, 2001, 203-259.
4. Foltz, P.W. Using latent semantic indexing for information filtering. In ACM SIG-OIS, 1990, 40-47.
5. Mooney, R.J., Roy, L. Content-based book recommending using learning for text categorization. In Proceedings of the 5th ACM Conference on Digital Libraries, June 2000, San Antonio, Texas, USA. 195-204.
6. Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A., Cohen, M.D. Intelligent information sharing systems. Communications of the ACM, 30(5), 1987, 390-402.
7. Jennings, A., Higuchi, H. A personal news service based on a user model neural network. IEICE Transactions on Information and Systems. E75-D(2) 198-210.
8. Desjardins G, Godin R. Combining relevance feedback and genetic algorithms in an Internet information personalization engine. RIAO'2000 Conference Proceedings. Vol.2, 2000, Paris, France.
9. Abidi, S.S.R., Han, C. Constraint Satisfaction Methods for Information Personalization. In A. Tawfik, S. Goodwin (Eds.) Lecture Notes in Artificial Intelligence 3060, 17tth Canadian Conference, on Artificial Intelligence, London, Ontario, 2004.
10. Abidi, S.S.R., Han, C. An Adaptive Hypermedia System for Information Customization via Content Adaptation. IADIS International Journal of WWW/Internet, 2(1), 2004, 79-94
11. Han, J.W., Kamber, M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2000.
12. Tsang E, Foundations of constraint satisfaction. Academic Press, London, UK. 1993.
13. Barták, R. Constraint programming: In pursuit of the holy grail. Proceedings of the Week of Doctoral Students (WDS99), Part IV, MatFyz Press, Prague, 1999, 555-564.
14. Torrens, M., Faltings, B. SmartClients: Constraint satisfaction as a paradigm for scaleable intelligent information systems. Workshop on Artificial Intelligence on Electronic Commerce, AAAI-99, 1999, Florida, USA.
15. Padmanabhuni, S., You, J.H., Ghose, A. A framework for learning constraints. Proc. of the PRICAI Workshop on Induction of Complex Representations, August 1996.
16. O'Sullivan, B., Freuder, E.G., O'Connell, S. Interactive Constraint Acquisition. In Workshop on User-Interaction in Constraint Satisfaction, Seventh International Conference on Principles and Practice of Constraint Programming - CP 2001, 2001.
17. Brin, S., Motwani, R., Silverstein, C. Beyond Market Baskets - Generalizing Association Rules to Correlations. In Proceedings of the ACM SIGMOD, 1997.
18. Freuder, E., Wallace, R. Partial Constraint Satisfaction. Artificial Intelligence, Vol. 58, 1992, 21-70.
19. Aarts, E., Lenstra, J.K., eds. Local search in combinatorial optimization. Princeton University Press, Princeton, NJ, 2003.
20. Meseguer, P., Bouhmala, N., Bouzoubaa, T., Irgens, M., Sanchez, M. Current Approaches for Solving Over-Constrained Problems. Constraints, Vol. 8, 2003, 9-39.