(3)Improving the effect of association by analyzing the /3/ Marko Balabanovic. An adaptive web page equence of a web user's queries and understanding recommendation service, In proceedings of /international their intentions conference on autonomous agent, Marina del Rey, ca, 199 (4)Using the taxonomic architecture of web search [41 Chumki Basu, Haym Hirsh and William Cohen. engine to create the superclass and subclass Recommendation as classification: using social and content association concepts of a key word automatically. based information in recommendation, In proceedings AAAL98,1998 (5) Studying the implementation of the XML-based [5) Eric I. Glover, Steve Lawrence, Michael D. Gordon conceptual association. William P. Birmingham and C. Lee Giles. Recommendin web documents base ed on user preferences, In e have achieved the preparation of part of the above of SiGIr 99 Workshop on Recommender Systems, 1999 work,and will keep informing our latest developments [6] T.Mitchell. Machine Learning. MeGram Hill, 1997 henceforth [7 Mil International Jounal of Lexicography, 3(4), 1990 9. References [8] Richardson S. D, Dolan w. B. and andervende L MindNet: acquiring and structuring semantic information from text, In Proc. of COLING-ACL 98, 1098-1102, 1998 [1] Venkat N. Gudivada et al.: Information retrieval on the [9] Euro WordNet: Building a multilingual database with World Wide Web. In proceedings of IEEE Internet severa European languages Computing,1997,1{5}:55-68 [2] Christos Faloutsos and Douglas Oard surveyof[10]Hownet.http formation retrieval and filtering methods, technical report, [11] Zhou Qiang, Feng Songyan. Build a relation network http:/citeseernj.neccomm/faloutsos96survey.html representation for Hownet, journal of chinese information processing, 2000(5) Proceedings of the 4th IEEE Int'l Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems(WECWIS 2002) 1530-1354/02$1700e2002EE SOCIE(3) Improving the effect of association by analyzing the sequence of a web user’s queries and understanding their intentions. (4) Using the taxonomic architecture of web search engine to create the superclass and subclass association concepts of a key word automatically. (5) Studying the implementation of the XML-based conceptual association. We have achieved the preparation of part of the above work, and will keep informing our latest developments henceforth. 9. References [1] Venkat N. Gudivada et al.: Information retrieval on the World Wide Web. In proceedings of IEEE Internet Computing, 1997, 1{5}:55-68. [2] Christos Faloutsos and Douglas Oard. A survey of information retrieval and filtering methods, technical report, http://citeseer.nj.nec.com/faloutsos96survey.html. [3] Marko Balabanovic. An adaptive web page recommendation service, In proceedings of 1st International conference on autonomous agent, Marina del Rey, ca, 1997. [4] Chumki Basu, Haym Hirsh and William Cohen. Recommendation as classification: using social and content based information in recommendation, In proceedings of AAAI-98, 1998.. [5] Eric J. Glover, Steve Lawrence, Michael D. Gordon, William P. Birmingham and C. Lee Giles. Recommending web documents based on user preferences, In proceedings of SIGIR 99 Workshop on Recommender Systems, 1999. [6] T. Mitchell. Machine Learning. McGram Hill, 1997. [7] Miller G. Wordnet: An On-line Lexical Database. International Journal of Lexicography, 3(4), 1990. [8] Richardson S. D., Dolan W. B. and Vandervende L. MindNet: acquiring and structuring semantic information from text, In Proc. of COLING-ACL’98, 1098-1102, 1998. [9] EuroWordNet: Building a multilingual database with wordnets for several European languages. http://www.hum.uva.nl/~ewn/. [10] Hownet. http://www.keenage.com/ [11] Zhou Qiang, Feng Songyan. Build a relation network representation for Hownet, journal of chinese information processing, 2000(5). Proceedings of the 4th IEEE Int’l Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS 2002) 1530-1354/02 $17.00 © 2002 IEEE