Out main goal is to improve the quality of web search engines. In 1994, some people believed that a complete search index would make it possible to find anything easily. According to Best of the Web 1994 -- Navigators, "The best navigation service should make it easy to find almost anything on the Web(once all the data is entered)." However, the Web of 1997 is quite different. Anyone who has used a search engine recently, can readily testify that the completeness of the index is not the only factor in the quality of search results. "Junk results" often wash out any results that a user is interested in. In fact, as of November 1997, only one of the top four commercial search engines finds itself (returns its own search page in response to its name in the top ten results). One of the main cause of this problem is that the number of documents in the indices has been increasing by many orders of magnitude, but the user's ability to look at documents has not. People are still only willing to look at the first few tens of results. Because of this, as the collection size grows, we need tools that have very high precision ( number of relevant documents returned, say in the top ten of results). Indeed, we want our notion of "relevant" to only inculde the very best documents since there even at the expense of recall ( the total number of relevant documents the system is able to return ). There is quite a bit of recent optimism that the use of more hypertextual information for making relevance judgments and other applications [Marchiori 97], [Spertus 97], [Weiss 96], [Kleinberg 98]. In particular, link structure [Page 98] and link text provide a lot of information for making relevance judgements and quality filtering. Google makes use of both link structure and anchor text .

PR(A) = (1-d)/N + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))

다른 페이지들의 PageRank 를 구하는 건 재귀호출 방식으로 반복. 

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