FoSSicker: A personalized search engine by location-awareness

With the rapid growth of Web documents collection, achieving high precision at the top retrieved documents has become a major issue for the search engine users, especially for the different needs of users for the same query. The typical search engines retrieve the same search results to users that cannot satisfy users any more. In this paper, we introduce a location-aware search engine based on machine learning (FoSSicker) to enhance the precision of web search which takes information parsed from IP address as context to personalize the search results. Moreover, we use history (users´ clickthrough data) to intelligentize the search engine to top the Web document which is mostly wanted. By experiments, this intelligent search engine is proved to be a faster reactor compared with the existing personalized search engines.