Optimizing web search using social annotations

  • Authors:

  • Shenghua Bao;Guirong Xue;Xiaoyuan Wu;Yong Yu;Ben Fei;Zhong Su

  • Affiliations:

  • Shanghai Jiao Tong University;Shanghai Jiao Tong University;Shanghai Jiao Tong University;Shanghai Jiao Tong University;IBM China;IBM China

  • Venue:
  • Proceedings of the 16th international conference on World Wide Web
  • Year:
  • 2007

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Abstract

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.