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• W2113103487 abstract "Many search engine users face problems while retrieving their required Information. For example, a user may find it is difficult to retrieve sufficient relevant information because he use too few keywords to search or the user is inexperienced and do not search using proper keywords and the search engine is not able to receive the user real meaning through his given keywords. Also, due to the recent improvements of search engines and the rapid growth of the web, the search engines return a huge number of web pages, and then the user may take long time to look at all of these pages to find his needed information. The problem of obtaining relevant results in web searching has been tackled by several approaches. Although very effective techniques are currently used by the most popular search engines, but no a priori knowledge on the user’s desires beside the search keywords is available. In this paper, we present an approach for optimizing the search engine results using artificial intelligence techniques such as document clustering and genetic algorithm to provide the user with the most relevant pages to the search query. The proposed method uses the Meta-data that is coming from the user preferences or the search engine query log files. These data is important to find the most related information to the user while searching the web. Finally, the method implementation and some of the experimental results are presented with the conclusion of this research study." @default.
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• W2113103487 date "2012-07-28" @default.
• W2113103487 modified "2023-09-30" @default.
• W2113103487 title "Optimizing Search Engine Result using Intelligent Model" @default.
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• W2113103487 doi "https://doi.org/10.5120/7736-0788" @default.
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