Abstract
The main goal of this research was to investigate means of intelligent support for retrieval of web documents. We have proposed the architecture of the web tool system — Trillian, which discovers the interests of users without their interaction and uses them for autonomous searching of related web content. Discovered pages are suggested to the user. The discovery of user interests is based on analysis of documents that users had visited in the past. We have shown that clustering is a feasible technique for extraction of interests from web documents. We consider the proposed architecture to be quite promising and suitable for future extensions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M.S. Chen, J.S. Park, and P.S. Yu: Efficient Data Mining for Path Traversal Patterns. IEEE Transaction on Knowledge and Data Engineering 10(2):209–221, 1998.
D.W. Cheung, B. Kao, and J. Lee: Discovering User Access Patterns on the World Wide Web. Knowledge Based Systems, 10:463–470, 1998.
R. Koval: Intelligent Support for Information Retrieval in WWW Environment. Master’s thesis. Slovak University of Technology Department of Computer Science and Engineering 1999.
Y. Lashkari: Feature Guided Automated Collaborative Filtering. Master’s thesis. MIT Department of Media Arts and Sciences 1995.
W. Lou, G. Liu, H. Lu, and Q. Yang: Cut-and-Pick Transactions for Proxy Log Mining. Proceedings 8 th EDBT Conference, Springer LNCS 2287, pp. 88–105, 2002.
A. Nanopoulos and Y. Manolopoulos: Finding Generalized Path Patterns for Web Log Data Mining. Proceedings 4 th ADBIS Conference, Springer LNCS 1884, pp. 215–228, 2000.
J. Pei, J. Han, B. Mortazavi-asl, and H. Zhu: Mining Access Patterns Efficiently from Web Logs. Proceedings 4 th PAKDD Conference, Springer LNCS 1805, pp. 396–407, 2000.
G. Polcicova: Recommending HTML-documents Using Feature Guided Automated Collaborative Filtering. Proceedings 3rd ADBIS Conference, Short Papers. Maribor, pp. 86–91, 1999.
G. Polcicova and P. Návrat: Recommending WWW Information Sources Using Feature Guided Automated Collaborative Filtering. Proceedings Conference on Intelligent Information Processing at the 16th IFIP World Computer Congress, pp. 115–118, Beijing, 2000.
G. Polcicova, R. Slovak, and P. Návrat: Combining Content-based and Collaborative Filtering. Proceedings 4th ADBIS Conference, Challenges papers, pp. 118–127, Prague, 2000.
C.J. van Rijsbergen: Information Retrieval, Butterworths, London, 1979.
E. Ukkonen: On-line Construction of Suffix Trees. Algorithmica, 14:249–260, 1995.
L. Ungar and D. Foster: Clustering Methods for Collaborative Filtering. Proceedings AAAI Workshop on Recommendation Systems, 1998
H. Yu, L. Breslau, and S. Shenker: A Scalable Web Cache Consistency Architecture. Proceedings ACM SIGCOMM Conference, 29 (4):163–174, 1999.
O. Zamir and O. Etzioni: Web Document Clustering: A Feasibility Demonstration. Proceedings 19th ACM SIGIR Conference, pp.46–54, 1998.
O. Zamir: Clustering Web Documents: A Phrase Based Method for Grouping Search Engine Results, University of Washington, 1999
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koval, R., Návrat, P. (2002). Intelligent Support for Information Retrieval in the WWW Environment. In: Manolopoulos, Y., Návrat, P. (eds) Advances in Databases and Information Systems. ADBIS 2002. Lecture Notes in Computer Science, vol 2435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45710-0_5
Download citation
DOI: https://doi.org/10.1007/3-540-45710-0_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44138-0
Online ISBN: 978-3-540-45710-7
eBook Packages: Springer Book Archive