Recommendation strategy based on relation rule mining

WSEAS Transactions on Information Science and Applications

Volumn 3, Issue 1, 2006, Pages 7-14

  Adda, Mehdi ^a,b   Djeraba, Chabane ^b  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b UNIV LILLE   (France)

Author keywords

Data mining; Ontology; Personalization; Recommendation systems

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; INFORMATION RETRIEVAL; KNOWLEDGE REPRESENTATION; SEMANTICS; WORLD WIDE WEB;

ONTOLOGY; PERSONALIZATION; RECOMMENDATION SYSTEMS;

DATA MINING;

EID: 30144444015     PISSN: 17900832     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. C. C. Aggarwal, J. L. Wolf, K. Wu. and P. S. Yu. Horting hatches an egg: A new graph-theoretic approch to collaborative filtering. Fifth ACM SIGKDD international conference on Knowledge discovery and data mining, California. United States, 1999.
2. R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 207-216, 1993.
3. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. 20th Int. Conference on Very Large Data Bases, VLDB94, 1994,
4. R. Agrawal and R. Srikant. Mining sequential patterns. Proceedings of the Eleventh International Conference on Data Engineering, IEEE Computer Society., pages 3-14, 1995.
5. J. Ayres, J. Gehrke, T. Yiu, and J. Flannick. Sequential pattern mining using a bitmap representation. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, July 2002.
6. F. Baader, D. Calvanese, D. McGuinness. D. Nardi, and P. Patel-Schneider. The description logic handbook: Theory, implementation and applications. Cambridge University Press, page 574, 2003.
7. T. Berners-Lee, J. Hendler, and O. Lassila. The semantic web. Scientific American, May 2001.
8. J. S. Breese, D. Hackermann, and C. KADIE analysis of predictive algorithms for collaborative filtering. Proceeding of the fourteenth conference on Uncertainty in Artificial Intelligence, pages 43-52, 1998.
9. DAML Joint Commette. Daml+oil language. http://www.daml.org/2001/03/ daml+oil-index.html, March 2001.
10. H. Dai and B. Mobasher. Integrating semantic knowledge with web usage mining for personalization. Integrating Semantic Knowledge with Web Usage Mining for Personalization, Proceedings of the AAAI 2004 Workshop on Semantic Web Personalization (SWP '04), july 2004.
11. S. Decker and M. Sintek. Semanticweb. http://www.semanticweb.org, 2005.
12. G. Graef and C. Schaefer. Application of art2 networks and self-organizing maps to collaborative filtering. 7th International Workshop on Open Hypermedia Systems, Austria, 2001.
13. J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. Proc. of International Conf. on Very Large Data Bases VLDB'95, Zürich, Switzerland, pages 420-431, 1995.
14. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. ACM SIGMOD Intl. Conference on Management of Data, 2:1-12, 2000.
15. I. Horrocks, F. van Harmelen, and P. Patel-Schneider. Ontology-based information exchange for knowledge management and electronic commerce. http://ontoweb.ontoware.org/, 2005.
16. J. Kleinberg and M. Sandler. Convergent algorithms for collaborative filtering. Convergent Algorithms for Collaborative Filtering, 4th ACM conference on electronic commerce., 2003.
17. M. Leleu, C. Rigotti, J-F. Boulicaut, and G. Euvrard. Go-spade: mining sequential patterns over datasets with consecutive repetitions. Proceedings of the IAPR Third International Conference on Machine Learning and Data Mining in Pattern Recognition MLDM'03, Leipzig (Germany), pages 293-306, July 2003.
18. W. Lin, S. A. Alvarez, and C. Ruiz. Collaborative recommendation via adaptive association rule mining. Worskhop on Web Mining for E-Commerce- Challenges and Opportunities, United States, 2000.
19. D. L. McGuinness and F. van Harmelen. Owl web ontology language overview. http://www.w3.org/TR/owlfeatures/, 2004.
20. R.V. Meteren and M.V. Someren. Using content-based filtering for recommendation. Machine Learning in the New Information Age MLnet/ ECML2000 Workshop, Spain, 2000.
21. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. Tenth international conference on Worm Wide Web, Hong Kong, 2001.
22. U. Shardanand and P. Maes. Social information filtering: Algorithms for automating "word of mouth". Proceedings of the SIGCHI conference on Human factors in computing systems, USA, 1995.
23. R. Srikant and R. Agrawal. Mining generalized association rules. In Proceedings of the 21st Int'l Conference on Very Large Databases (VLDB95). Zurich. Switzerland., September 1995.
24. R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. Proc. 5th Int. Conf. Extending Database Technology, EDBT, Avigon, France, 1057:3-17, 1996.
25. R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. Proc. of the 3rd International Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach, California, 1997.