A personalized search engine based on web-snippet hierarchical clustering

14th International World Wide Web Conference, WWW2005

Volumn , Issue , 2005, Pages 801-810

  Ferragina, Paolo ^a   Gulli, Antonio ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

Information extraction; New search applications and interfaces; Personalized web ranking; Search engines; Web snippets clustering

Indexed keywords

CLUSTERING ENGINES; HIER-ARCHICAL CLUSTERING; INFORMATION EXTRACTION; NEW SEARCH APPLICATIONS AND INTERFACES; NON-INTRUSIVE; ON-THE-FLY; OPEN SOURCE SYSTEM; PERSONALIZATIONS; PERSONALIZED SEARCH; PRIVACY PRESERVING; SEARCH APPLICATION; SEARCH RESULTS;

ENGINES; INFORMATION RETRIEVAL; SEARCH ENGINES;

WORLD WIDE WEB;

EID: 77953048404     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1062745.1062760     Document Type: Conference Paper

References (35)

1. http://labs.google.com/personalized.
2. http://mysearch.yahoo.com/.
3. G. Attardi, A. Gulli, and F. Sebastiani. Theseus: categorization by context. In WWW8, 1999.
4. K. Bharat and A. Broder. A technique for measuring the relative size and overlap of public web search engines. In WWW7, 1998.
5. A. Broder. A taxonomy of web search. In SIGIR Forum 36, 2002.
6. C. Carpineto and G. Romano. Concept Data Analysis: Theory and Applications. John Wiley & Sons, 2004.
7. H. Chen and S. T. Dumais. Bringing order to the web: automatically categorizing search results. In SIGCHI00.
8. P. A. Chirita, D. Olmedilla, and W. Nejdl. PROS: A personalized ranking platform for web search. In Int. Conf. on Adaptive Hypermedia and Web-based Syst., 2004.
9. SnakeT Dataset. http://roquefort.di.unipi.it/~gulli/ listAllowed/ testSnakeT/.
10. B. Fung, K. Wang, and M. Ester. Large hierarchical document clustering using frequent itemsets. In SDM03.
11. F. Giannotti, M. Nanni, and D. Pedreschi. Webcat: Automatic categorization of web search results. In SEBD03.
12. J. Grabmeier and A. Rudolph. Techniques of cluster algorithms in data mining. In Data Mining and Knowledge Discovery, volume 6(4), pages 303-360, 2002.
13. M. Halkidi, Y. Batistakis, and M. Vazirgiannis. On clustering validation techniques. In JIIS, 2001.
14. T. Haveliwala. Topic-sensitive pagerank. In WWW12, 2002.
15. M. A. Hearst and J. O. Pedersen. Reexamining the cluster hypothesis: Scatter/gather on retrieval results. In SIGIR-96.
16. G. Jeh and J. Widom. Scaling personalized Web search. In WWW13, 2003.
17. Z. Jiang, A. Joshi, R. Krishnapuram, and L. Yi. Retriever: Improving web search engine results using clustering. In Managing Business with Electronic Commerce 02.
18. J. Kleinberg. Authoritative sources in a hyperlinked environment. In JASM, 1999.
19. K. Kummamuru, R. Lotlikar, S. Roy, K. Singal, and R. Krishnapuram. A hierarchical monothetic document clustering algorithm for summarization and browsing search results. In WWW13, 2004.
20. D. J. Lawrie. Language Models for Hierarchical Summarization. PhD thesis, Amherst, 2003.
21. D. J. Lawrie and W. B. Croft. Generating hiearchical summaries for web searches. In SIGIR03.
22. Y. S. Maarek, R. Fagin, I. Z. Ben-Shaul, and D. Pelleg. Ephemeral document clustering for web applications. Technical Report RJ 10186, IBM Research, 2000.
23. M. Meila. Comparing clusterings. Technical Report 418, University of Washington, 2002.
24. Javed Mostafa. Seeking better web searches. Scientific American, February 2005.
25. S. Osinski and D. Weiss. Conceptual clustering using lingo algorithm: Evaluation on open directory project data. In IIPWM04, 2004.
26. SnakeT Test Results. http: //roquefort.di.unipi.it/~gulli/listAllowed/ testing/.
27. G. Salton and M. McGill. Introduction to Modern Information Retrieval. McGraw Hill, 1983.
28. http://www.searchenginelowdown.com/2004/10/ web-20-exclusive- demonstration-of.html.
29. http://www.betanews.com/article/ Microsoft Tests Search Clustering/1106319504.
30. Y. Wang and M. Kitsuregawa. On combining link and contents information for web page clustering. In DEXA02.
31. D. Weiss and J. Stefanowski. Web search results clustering in polish: Experimental evaluation of carrot. In IIS03.
32. Y. Wu and X. Chen. Extracting features from web search returned hits for hierarchical classification. In IKE03.
33. O. Zamir and O. Etzioni. Grouper: a dynamic clustering interface to Web search results. In WWW8, 1999.
34. H. Zeng, Q. He, Z. Chen, and W. Ma. Learning to cluster web search results. In SIGIR04.
35. D. Zhang and Y. Dong. Semantic, hierarchical, online clustering of web search results. In WIDM01.