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Web page vectorization
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With the rapid development of the Internet, the process of finding useful information quickly becomes more important. Some search engines meet the search needs of users. However, they don't think about the user's interest or background. As a result, a large number of irrelevant web pages are retrieved from user queries. The usefulness of a search engine depends on its relevance to the returned result set. While there may be millions of web pages that contain specific words or phrases, some may be more relevant, popular, or authoritative than others. Most search engines use methods to rank results on Web pages in order to provide the most relevant results. There is a difference to another kind of search engine: how does the search engine decide which pages are the best match and the order in which these web pages will be displayed to the user. These methods also change as Internet usage changes over time and new technologies develop. In this paper, the optimization of artificial intelligence (AI) technology is proposed to improve the results returned by the search engine. They can be able to optimize and return results that the user is most interested in. Page clustering is to group all similar pages together, and then genetic algorithm is applied to the most relevant pages from each group. The short final list of the best web pages is displayed to the user, so that the information needed can be found easily and quickly. In addition, the query recommendation method is applied to help users type search queries. The query recommendation algorithm indicates that the previous related query finds the list of queries submitted by users. Retrieving questions about web pages is not a search engine method, but it strongly depends on the query. More accurate search and query users will get more relevant results. Finally, the proposed methods have been implemented to optimize the search results, and the experimental results have been analyzed to evaluate the performance of these methods.
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