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WWW Search Engines

WWW Search Engines. Indexing for unstructured resources Nils Rooijmans 25-4-2006 @ TU Eindhoven. www.ilse.nl. Founded as first dutch SE in 1996 Number 1 SE in the Netherlands, nowadays nr 2 next to Google 80 mio web url’s in index 1 mio queries per day

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WWW Search Engines

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  1. WWW Search Engines Indexing for unstructured resources Nils Rooijmans 25-4-2006 @ TU Eindhoven

  2. www.ilse.nl • Founded as first dutch SE in 1996 • Number 1 SE in the Netherlands, nowadays nr 2 next to Google • 80 mio web url’s in index • 1 mio queries per day • 7 developpers in Eindhoven (most formely TUE) • BusinessModel: Paid Listings

  3. Global Architecture • Functional Architecture • SE_Architecture.htm • System architecture complex because of scalability issues

  4. Spider/Crawler • Initial seed • Link extraction • Spider fence • URL DB (prioritising, scheduling) • HTTP response handlers • Robots.txt

  5. Parser • HTML is a mess • Webpages have different formats • PDF • Word/Powerpoints/txt • Flash • Javascript! • CSS

  6. Indexing • Vector Space model • Indexfiles • Precompute Ranking Properties • Title/URL/Body flags • Anchor texts • TF/IDF

  7. Retrieval • Term based ranking: • Position and proximity of queryterms • TF*IDF • Achor texts • Link Based ranking: • The web is not just a collection of documents – its hyperlinks are important!

  8. PageRank - Motivation • A link from page A to page B is a vote of the author of A for B, or a recommendation of the page. • The number incoming links to a page is a measure of importance and authority of the page. • Also take into account the quality of recommendation, so a page is more important if the sources of its incomoing llinks are important.

  9. What is a Markov Chain? • A Markov chain has two components: • A network structure much like a web site, where each node is called a state. So the complete web is the set of all possible states. • A transition probability of traversing a link given that the chain is in a state. • For each state the sum of outgoing probabilities is one. • A sequence of steps through the chain is called a random walk.

  10. The Random Surfer • Assume the web is a Markov chain. • Surfers randomly click on links, where the probability of an outlink from page A is 1/m, where m is the number of outlinks from A. • The surfer occasionally gets bored and is teleported to another web page, say B, where B is equally likely to be any page. • Using the theory of Markov chains it can be shown that if the surfer follows links for long enough, the PageRank of a web page is the probability that the surfer will visit that page.

  11. Dangling Pages • Problem: A and B have no outlinks. Solution: Assume A and B have links to all web pages with equal probability.

  12. Rank Sink • Problem: Pages in a loop accumulate rank but do not distribute it. • Solution: Teleportation, i.e. with a certain probability the surfer can jump to any other web page to get out of the loop.

  13. PageRank (PR) - Definition • P is a web page • Pi are the web pages that have a link to P • O(Pi) is the number of outlinks from Pi • d is the teleportation probability • N is the size of the web

  14. Example Web Graph

  15. Iteratively Computing PageRank • Replace d/N in the def. of PR(P) by d, so PR will take values between 1 and N. • d is normally set to 0.15, but for simplicity lets set it to 0.5 • Set initial PR values to 1 • Solve the following equations iteratively:

  16. Example Computation of PR

  17. Large Matrix Computation • Computing PageRank can be done via matrix multiplication, where the matrix has 30 million rows and columns. • The matrix is sparse as average number of outlinks is between 7 and 8. • Setting d = 0.15 or above requires at most 100 iterations to convergence. • Researchers still trying to speed-up the computation.

  18. Link Spamming to Improve PageRank • Spam is the act of trying unfairly to gain a high ranking on a search engine for a web page without improving the user experience. • Link farms - join the farm by copying a hub page which links to all members. • Selling links from sites with high PageRank.

  19. Result Optimization by Machine Learning • Future: personalized search by learning from user feedback • Which ranking features work well? • LEARN IT!

  20. Assignment • Build a meta-search • Learn which algorithm works best for which type of queries: • single VS multi-word queries • general queries VS specific queries(roughly based on the number of results in the resultset) • Improve ranking

  21. Step by step approach • Build website based on API’s • Ilse API (mail searchengine@ilse.net for documentation) • Google API: http://www.google.com/apis/index.html • Yahoo API: http://developer.yahoo.net/web/V1/webSearch.html • Merge resultsets (handle duplicates, hide originating API(s)) • Build feedback mechanism • Score different API’s for different query types • Use score in merging of results

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