| Taher H. Haveliwala. E#cient computation of PageRank. Technical report, Computer Science Department, Stanford University, 1999. |
....to be developed. At the other end of the spectrum, with the increasing importance of personalization, the ability to build some of these indexes and measures on a smaller scale (customized for individuals or small groups of users and using limited resources) also becomes important. For example, [33] discusses some techniques for e#ciently evaluating PageRank on modestly equipped machines. 5 Ranking and Link Analysis As shown in Figure 1, the Query Engine collects search terms from the user and retrieves pages that are likely to be relevant. As mentioned in Section 1, there are two main ....
....Also, note that we are more interested in the relative ordering of the pages induced by the PageRank (since this is used to rank the pages) than the actual PageRank values themselves. Thus we can terminate the power iteration once the ordering of the pages becomes reasonably stable. Experiments [33] indicate that the ordering induced by the PageRank converges much faster than the actual PageRank. 5.1.6 Using PageRank for keyword searching In [10] Brin and Page describe a prototype search engine they developed at Stanford called Google (which has subsequently become the search engine ....
Taher Haveliwala. E#cient computation of pagerank. Technical Report 1999-31, Database Group, Computer Science Department, Stanford University, February 1999. Available at http://dbpubs. stanford.edu/pub/1999-31.
No context found.
Taher H. Haveliwala. E#cient computation of PageRank. Technical report, Computer Science Department, Stanford University, 1999.
No context found.
Taher H. Haveliwala. E#cient computation of PageRank. Technical report, Computer Science Department, Stanford University, 1999.
No context found.
Taher H. Haveliwala. E#cient computation of PageRank. Technical report, Computer Science Department, Stanford University, 1999.
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