Proceedings of the 11th international conference on World Wide Web
ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient search ranking in social networks
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A ranking algorithm for online social network search
Proceedings of the 6th ACM India Computing Convention
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Social Network Services(SNS) such as Facebook, Friendster, MySpace and Orkut have established themselves as very popular and powerful tools for making and finding friends and for identifying other people who share similar interests. Search behavior of Web users often reflects that of others who have similar interests or similar information profiles in social networks. Therefore, if we locate web users interested in certain topics or areas and then keep track of their preferences in terms of Web search results, we can pinpoint relevant and reliable information about these people in a timely manner In this paper, we present an efficient search system called SMART Finder. This system supports efficient search results for locating people whose social relationships are highly ranked according to specific topics. It can also identify people who are highly associated with each other with regard to Web search topics. Our results are supported by experiments on Orkut and Facebook.