Optimizing Web Search Using Social Annotations
- Shenghua Bao (Shanghai Jiao Tong University)
- Xiaoyuan Wu (Shanghai Jiao Tong University)
- Ben Fei (IBM China Research Lab)
- Gui-Rong Xue (Shanghai Jiao Tong University)
- Zhong Su (IBM China Research Lab)
- Yong Yu (Shanghai Jiao Tong University)
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 observed that the social annotations can benefit the 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 these 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 empirically evaluate the proposed methods with 50 manually annotated queries and 3000 auto-generated queries, on a dataset consisting of 690,482 web pages with 2,879,614 different annotations. Experiments show that both SSR and SPR benefit the web search significantly. By incorporating both the SPR and SSR features, the quality of search results can be improved by as much as 14.80% and 25.02% compared with the original performance in MAP on two query sets respectively.