TY  - CONF
AU  - Bao, Shenghua
AU  - Xue, Guirong
AU  - Wu, Xiaoyuan
AU  - Yu, Yong
AU  - Fei, Ben
AU  - Su, Zhong
A2  - 
T1  - Optimizing web search using social annotations
T2  - WWW '07: Proceedings of the 16th international conference on World Wide Web
PB  - ACM Press
C1  - New York, NY, USA
PY  - 2007/
CY  -  
VL  - 
IS  - 
SP  - 
EP  - 
UR  - 
DO  - 
KW  - tagging
KW  - taggingsurvey
KW  - social
KW  - bookmarking
KW  - annotations
KW  - search
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 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.
ER  -