An adaptive search engine considering user's community

摘要

Search engines are broadly used by people who take part in different social networks. Information in social networks can be used to optimize search results. Friendship links in a social network creates a graph of communities. A community in network contains users that are like one another and unlike nodes containing in the further communities. Chronically, similar vertices have common neighbors. In a social network if two users have lots of common friends, they possibly share an interest. As a result, they search for common information and one user can gain profit from the other's search process. In this paper, we try to optimize search engine process. We consider the visit rate of Web pages by community-mates (users in same community) as a criterion to rank the search results. Experimental results show that more relevant page results are presented in higher ranks by our search engine.