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lecture22 - Lecture 22 SVD Eigenvector and Web Search...

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Lecture 22 SVD, Eigenvector, and Web Search Shang-Hua Teng
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Earlier Search Engines Hotbot, Yahoo, Alta Vista, Northern Light, Excite, Infoseek, Lycos … Main technique: “inverted index” Conceptually: use a matrix to represent how many times a term appears in one page # of columns = # of pages (huge!) # of rows = # of terms (also huge!) Page1 Page2 Page3 Page4 ‘car’ 1 0 1 0 ‘toyota’ 0 2 0 1 page 2 mentions ‘toyota’ twice ‘honda’ 2 1 0 0
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Search by Keywords If the query has one keyword, just return all the pages that have the word E.g., “ toyota all pages containing “toyota”: page2, page4,… There could be many many pages! Solution: return those pages with most frequencies of the word first
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Multi-keyword Search For each keyword W , find all the set of pages mentioning W Intersect all the sets of pages Assuming an “AND” operation of those keywords Example: A search “ toyota honda ” will return all the pages that mention both “ toyota ” and “ honda
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Observations The “matrix” can be huge : Now the Web has more than 10 billion pages! There are many “terms” on the Web. Many of them are typos. It’s not easy to do the computation efficiently: Given a word, find all the pages… Intersect many sets of pages… For these reasons, search engines never store this “matrix” so naively.
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Problems Spamming: People want their pages to be put very top on a word search (e.g., “toyota”) by repeating the word many many times Though these pages may be unimportant compared to www.toyota.com , even if the latter only mentions “toyota” only once (or 0 time). Search engines can be easily “fooled”
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Closer look at the problems Lacking the concept of “importance” of each page on each topic E.g.: a random page may not be as “important” as Yahoo’s main page. A link from Yahoo is hence most likely more important than a link from that random page But, how to capture the importance of a page? A guess: # of hits? where to get that info? # of inlinks to a page Google’s main idea.
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PageRank Intuition: The importance of each page should be decided by what other pages “say” about this page One naïve implementation: count the # of pages pointing to each page (i.e., # of inlinks) Problem: We can easily fool this technique by generating many dummy pages that point to a page
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Link Analysis The goal is to rank pages We want to take advantage of the link structure to do this Two main approaches Static: we will use the links to calculate a ranking of the pages offline ( Google ) Dynamic: we will use the links in the results of a search to dynamically determine a ranking ( IBM Clever – Huts and Authorities )
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The Link Graph View documents as graph nodes and the hyperlinks between documents
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