document: Optimizing web search using social annotations This paper explores the use of social annotations to improve websearch . Nowadays , many services , e.g. del. icio . us , have been developed for web users to organize and share their favorite webpages 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 webpages ; 2 ) the count of annotations indicates the popularity of webpages . Two novel algorithms are proposed to incorporate the above information into page ranking : 1 ) SocialSimRank ( SSR ) calculates the similarity between social annotations and webqueries ; 2 ) SocialPageRank ( SPR ) captures the popularity of webpages . 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 webpage 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 crawledfrom delicious . Experiments show that both SSR and SPRbenefit web search significantly .
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