A Novel Approach of Web Search Based on Community Wisdom

In this paper, we propose a novel approach for Web search based on the statistical information of local setting data of web browsers in a community. The members of the community share their local setting data of browsers and this enables them to take advantage of the peer community members´s opinions in their Web search. Then we develop a new scheme that combines PageRank´s link-based ranking scores with our proposed community based popularity scores for web sites. This hybrid scheme provides a rank- ordering method for search query results that integrates the content consumers´ opinions with the content producers´ opinions in a balanced manner. The users´ opinions of web sites provide a solid starting point of trust for combatting web spam and improving the quality of Web search.