This paper explores the use of social annotations to improve web
search. Nowadays, many services, e.g. del.icio.us, have been
developed for web users to organize and share their favorite web
pages on line by using social annotations. We observe that the
social annotations can benefit web search in two aspects: 1) the
annotations are usually good summaries of corresponding web
pages; 2) the count of annotations indicates the popularity of web
pages. Two novel algorithms are proposed to incorporate the
above information into page ranking: 1) SocialSimRank (SSR)
calculates the similarity between social annotations and web
queries; 2) SocialPageRank (SPR) captures the popularity of web
pages. Preliminary experimental results show that SSR can find
the latent semantic association between queries and annotations,
while SPR successfully measures the quality (popularity) of a web
page from the web users’ perspective. We further evaluate the
proposed methods empirically with 50 manually constructed
queries and 3000 auto-generated queries on a dataset crawled
from del.icio.us. Experiments show that both SSR and SPR
benefit web search significantly.
%0 Conference Paper
%1 bao2007optimizing
%A Bao, Shenghua
%A Xue, Guirong
%A Wu, Xiaoyuan
%A Yu, Yong
%A Fei, Ben
%A Su, Zhong
%B WWW '07: Proceedings of the 16th international conference on World Wide Web
%C New York, NY, USA
%D 2007
%I ACM
%K annotations mt social websearch
%P 501--510
%R http://doi.acm.org/10.1145/1242572.1242640
%T Optimizing web search using social annotations
%U http://portal.acm.org/citation.cfm?id=1242640
%X This paper explores the use of social annotations to improve web
search. Nowadays, many services, e.g. del.icio.us, have been
developed for web users to organize and share their favorite web
pages on line by using social annotations. We observe that the
social annotations can benefit web search in two aspects: 1) the
annotations are usually good summaries of corresponding web
pages; 2) the count of annotations indicates the popularity of web
pages. Two novel algorithms are proposed to incorporate the
above information into page ranking: 1) SocialSimRank (SSR)
calculates the similarity between social annotations and web
queries; 2) SocialPageRank (SPR) captures the popularity of web
pages. Preliminary experimental results show that SSR can find
the latent semantic association between queries and annotations,
while SPR successfully measures the quality (popularity) of a web
page from the web users’ perspective. We further evaluate the
proposed methods empirically with 50 manually constructed
queries and 3000 auto-generated queries on a dataset crawled
from del.icio.us. Experiments show that both SSR and SPR
benefit web search significantly.
%@ 978-1-59593-654-7
@inproceedings{bao2007optimizing,
abstract = {This paper explores the use of social annotations to improve web
search. Nowadays, many services, e.g. del.icio.us, have been
developed for web users to organize and share their favorite web
pages on line by using social annotations. We observe that the
social annotations can benefit web search in two aspects: 1) the
annotations are usually good summaries of corresponding web
pages; 2) the count of annotations indicates the popularity of web
pages. Two novel algorithms are proposed to incorporate the
above information into page ranking: 1) SocialSimRank (SSR)
calculates the similarity between social annotations and web
queries; 2) SocialPageRank (SPR) captures the popularity of web
pages. Preliminary experimental results show that SSR can find
the latent semantic association between queries and annotations,
while SPR successfully measures the quality (popularity) of a web
page from the web users’ perspective. We further evaluate the
proposed methods empirically with 50 manually constructed
queries and 3000 auto-generated queries on a dataset crawled
from del.icio.us. Experiments show that both SSR and SPR
benefit web search significantly.},
added-at = {2009-11-24T08:28:28.000+0100},
address = {New York, NY, USA},
author = {Bao, Shenghua and Xue, Guirong and Wu, Xiaoyuan and Yu, Yong and Fei, Ben and Su, Zhong},
biburl = {https://www.bibsonomy.org/bibtex/2b9966b9df0199a0b7b2d5a1b0d7560cb/ghp09},
booktitle = {WWW '07: Proceedings of the 16th international conference on World Wide Web},
doi = {http://doi.acm.org/10.1145/1242572.1242640},
interhash = {2cbdc7da88c90ef22468108c1f481159},
intrahash = {b9966b9df0199a0b7b2d5a1b0d7560cb},
isbn = {978-1-59593-654-7},
keywords = {annotations mt social websearch},
location = {Banff, Alberta, Canada},
pages = {501--510},
publisher = {ACM},
timestamp = {2009-11-24T08:28:40.000+0100},
title = {Optimizing web search using social annotations},
url = {http://portal.acm.org/citation.cfm?id=1242640},
year = 2007
}