J. Wu, and K. Aberer. Proceedings of International Conference on Web Engineering, ICWE 2003, volume 2722 of Lecture Notes in Computer Science, page 431--440. Springer Berlin Heidelberg, (2003)
DOI: 10.1007/3-540-45068-8_80
Abstract
Traditional ranking models used in Web search engines rely on a static snapshot of the Web graph, basically the link structure of the Web documents. However, visitors' browsing activities indicate the importance of a document. In the traditional static models, the information on document importance conveyed by interactive browsing is neglected. The nowadays Web server/surfer model lacks the ability to take advantage of user interaction for document ranking. We enhance the ordinary Web server/surfer model with a mechanism inspired by swarm intelligence to make it possible for the Web servers to interact with Web surfers and thus obtain a proper local ranking of Web documents. The proof-of-concept implementation of our idea demonstrates the potential of our model. The mechanism can be used directly in deployed Web servers which enable on-the-fly creation of rankings for Web documents local to a Web site. The local rankings can also be used1 as input for the generation of global Web rankings in a decentralized way.
%0 Conference Paper
%1 brusilovsky:wu2003swarm
%A Wu, Jie
%A Aberer, Karl
%B Proceedings of International Conference on Web Engineering, ICWE 2003
%D 2003
%E Lovelle, JuanManuelCueva
%E Rodr\'ıguez, BernardoMart\'ınGonzález
%E Gayo, JoseEmilioLabra
%E del Puerto Paule Ruiz, Mar\'ıa
%E Aguilar, LuisJoyanes
%I Springer Berlin Heidelberg
%K social-navigation social-search swarm
%P 431--440
%R 10.1007/3-540-45068-8_80
%T Swarm Intelligent Surfing in the Web
%U http://dx.doi.org/10.1007/3-540-45068-8_80
%V 2722
%X Traditional ranking models used in Web search engines rely on a static snapshot of the Web graph, basically the link structure of the Web documents. However, visitors' browsing activities indicate the importance of a document. In the traditional static models, the information on document importance conveyed by interactive browsing is neglected. The nowadays Web server/surfer model lacks the ability to take advantage of user interaction for document ranking. We enhance the ordinary Web server/surfer model with a mechanism inspired by swarm intelligence to make it possible for the Web servers to interact with Web surfers and thus obtain a proper local ranking of Web documents. The proof-of-concept implementation of our idea demonstrates the potential of our model. The mechanism can be used directly in deployed Web servers which enable on-the-fly creation of rankings for Web documents local to a Web site. The local rankings can also be used1 as input for the generation of global Web rankings in a decentralized way.
@inproceedings{brusilovsky:wu2003swarm,
abstract = {{Traditional ranking models used in Web search engines rely on a static snapshot of the Web graph, basically the link structure of the Web documents. However, visitors' browsing activities indicate the importance of a document. In the traditional static models, the information on document importance conveyed by interactive browsing is neglected. The nowadays Web server/surfer model lacks the ability to take advantage of user interaction for document ranking. We enhance the ordinary Web server/surfer model with a mechanism inspired by swarm intelligence to make it possible for the Web servers to interact with Web surfers and thus obtain a proper local ranking of Web documents. The proof-of-concept implementation of our idea demonstrates the potential of our model. The mechanism can be used directly in deployed Web servers which enable on-the-fly creation of rankings for Web documents local to a Web site. The local rankings can also be used1 as input for the generation of global Web rankings in a decentralized way.}},
added-at = {2018-03-19T12:24:51.000+0100},
author = {Wu, Jie and Aberer, Karl},
biburl = {https://www.bibsonomy.org/bibtex/283b61d69003d437be976eb8e89da8f0e/aho},
booktitle = {Proceedings of International Conference on Web Engineering, ICWE 2003},
citeulike-article-id = {14215042},
citeulike-linkout-0 = {http://dx.doi.org/10.1007/3-540-45068-8_80},
citeulike-linkout-1 = {http://link.springer.com/chapter/10.1007/3-540-45068-8_80},
doi = {10.1007/3-540-45068-8_80},
editor = {Lovelle, JuanManuelCueva and Rodr\'{\i}guez, BernardoMart\'{\i}nGonz\'{a}lez and Gayo, JoseEmilioLabra and del Puerto Paule Ruiz, Mar\'{\i}a and Aguilar, LuisJoyanes},
interhash = {3cc0d3ce2085c4b35add069bef31bbac},
intrahash = {83b61d69003d437be976eb8e89da8f0e},
keywords = {social-navigation social-search swarm},
location = {Oviedo, Spain},
pages = {431--440},
posted-at = {2016-11-26 14:50:51},
priority = {2},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Swarm Intelligent Surfing in the Web}},
url = {http://dx.doi.org/10.1007/3-540-45068-8_80},
volume = 2722,
year = 2003
}