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Web Search Engine
Web search engines index billions of web pages and handle hundreds of millions of searches per day. They use inverted indexes to quickly search text and return relevant results. Ranking algorithms consider factors like term frequency, popularity, and link analysis using PageRank to determine the most authoritative pages for a given query. Crawling software systematically explores the web by following links to discover and index new pages.
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Web Search Engine
- 1. Web Search EnginesChapter 27, Part C Based on Larson and Hearst’s slides at UC-Berkeley http://www.sims.berkeley.edu/courses/is202/f00/
- 2. Search Engine CharacteristicsUnedited – anyone can enter content Quality issues; Spam Varied information types Phone book, brochures, catalogs, dissertations, news reports, weather, all in one place! Different kinds of users Lexis-Nexis: Paying, professional searchers Online catalogs: Scholars searching scholarly literature Web: Every type of person with every type of goal Scale Hundreds of millions of searches/day; billions of docs
- 3. Web Search QueriesWeb search queries are short: ~2.4 words on average (Aug 2000) Has increased, was 1.7 (~1997) User Expectations: Many say “The first item shown should be what I want to see!” This works if the user has the most popular/common notion in mind, not otherwise.
- 4. Directories vs. SearchEngines Directories Hand-selected sites Search over the contents of the descriptions of the pages Organized in advance into categories Search Engines All pages in all sites Search over the contents of the pages themselves Organized in response to a query by relevance rankings or other scores
- 5. What about Ranking?Lots of variation here Often messy; details proprietary and fluctuating Combining subsets of: IR-style relevance: Based on term frequencies, proximities, position (e.g., in title), font, etc. Popularity information Link analysis information Most use a variant of vector space ranking to combine these. Here’s how it might work: Make a vector of weights for each feature Multiply this by the counts for each feature
- 6. Relevance: Going BeyondIR Page “popularity” (e.g., DirectHit) Frequently visited pages (in general) Frequently visited pages as a result of a query Link “co-citation” (e.g., Google) Which sites are linked to by other sites? Draws upon sociology research on bibliographic citations to identify “authoritative sources” Discussed further in Google case study
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- 8. Standard Web SearchEngine Architecture crawl the web create an inverted index Check for duplicates, store the documents Inverted index Search engine servers user query Show results To user DocIds
- 9. Inverted Indexes theIR Way
- 10. How Inverted Files Are Created Periodically rebuilt, static otherwise. Documents are parsed to extract tokens. These are saved with the Document ID. Now is the time for all good men to come to the aid of their country Doc 1 It was a dark and stormy night in the country manor. The time was past midnight Doc 2
- 11. How Inverted Files are Created After all documents have been parsed the inverted file is sorted alphabetically.
- 12. How Inverted Filesare Created Multiple term entries for a single document are merged. Within-document term frequency information is compiled.
- 13. How Inverted Filesare Created Finally, the file can be split into A Dictionary or Lexicon file and A Postings file
- 14. How Inverted Filesare Created Dictionary/Lexicon Postings
- 15. Inverted indexes Permitfast search for individual terms For each term, you get a list consisting of: document ID frequency of term in doc (optional) position of term in doc (optional) These lists can be used to solve Boolean queries: country -> d1, d2 manor -> d2 country AND manor -> d2 Also used for statistical ranking algorithms
- 16. Inverted Indexes forWeb Search Engines Inverted indexes are still used, even though the web is so huge. Some systems partition the indexes across different machines. Each machine handles different parts of the data. Other systems duplicate the data across many machines; queries are distributed among the machines. Most do a combination of these.
- 17. From description ofthe FAST search engine, by Knut Risvik http://www.infonortics.com/searchengines/sh00/risvik_files/frame.htm In this example, the data for the pages is partitioned across machines. Additionally, each partition is allocated multiple machines to handle the queries. Each row can handle 120 queries per second Each column can handle 7M pages To handle more queries, add another row.
- 18. Cascading Allocation ofCPUs A variation on this that produces a cost-savings: Put high-quality/common pages on many machines Put lower quality/less common pages on fewer machines Query goes to high quality machines first If no hits found there, go to other machines
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- 20. Web Crawlers Howdo the web search engines get all of the items they index? Main idea: Start with known sites Record information for these sites Follow the links from each site Record information found at new sites Repeat
- 21. Web Crawling AlgorithmMore precisely: Put a set of known sites on a queue Repeat the following until the queue is empty: Take the first page off of the queue If this page has not yet been processed: Record the information found on this page Positions of words, links going out, etc Add each link on the current page to the queue Record that this page has been processed Rule-of-thumb: 1 doc per minute per crawling server
- 22. Web Crawling IssuesKeep out signs A file called norobots.txt lists “off-limits” directories Freshness: Figure out which pages change often, and recrawl these often. Duplicates, virtual hosts, etc. Convert page contents with a hash function Compare new pages to the hash table Lots of problems Server unavailable; incorrect html; missing links; attempts to “fool” search engine by giving crawler a version of the page with lots of spurious terms added ... Web crawling is difficult to do robustly!
- 23. Google: A CaseStudy
- 24. Google’s Indexing The Indexer converts each doc into a collection of “hit lists” and puts these into “barrels”, sorted by docID. It also creates a database of “links”. Hit: <wordID, position in doc, font info, hit type> Hit type: Plain or fancy. Fancy hit: Occurs in URL, title, anchor text, metatag. Optimized representation of hits (2 bytes each). Sorter sorts each barrel by wordID to create the inverted index . It also creates a lexicon file. Lexicon: <wordID, offset into inverted index> Lexicon is mostly cached in-memory
- 25. Google’s Inverted IndexLexicon (in-memory) Postings (“Inverted barrels”, on disk) Each “barrel” contains postings for a range of wordids. Sorted by wordid Barrel i Barrel i+1 Sorted by Docid wordid #docs wordid #docs wordid #docs Docid #hits Hit, hit, hit, hit, hit Docid #hits Hit Docid #hits Hit Docid #hits Hit, hit, hit Docid #hits Hit, hit
- 26. Google Sorted barrels= inverted index Pagerank computed from link structure; combined with IR rank IR rank depends on TF, type of “hit”, hit proximity, etc. Billion documents Hundred million queries a day AND queries
- 27. Link Analysis forRanking Pages Assumption: If the pages pointing to this page are good, then this is also a good page. References: Kleinberg 98, Page et al. 98 Draws upon earlier research in sociology and bibliometrics. Kleinberg’s model includes “authorities” (highly referenced pages) and “hubs” (pages containing good reference lists). Google model is a version with no hubs, and is closely related to work on influence weights by Pinski-Narin (1976).
- 28. Link Analysis forRanking Pages Why does this work? The official Toyota site will be linked to by lots of other official (or high-quality) sites The best Toyota fan-club site probably also has many links pointing to it Less high-quality sites do not have as many high-quality sites linking to them
- 29. PageRank Let A1,A2, …, An be the pages that point to page A. Let C(P) be the # links out of page P. The PageRank (PR) of page A is defined as: PageRank is principal eigenvector of the link matrix of the web. Can be computed as the fixpoint of the above equation. PR(A) = (1-d) + d ( PR(A1)/C(A1) + … + PR(An)/C(An) )
- 30. PageRank: User ModelPageRanks form a probability distribution over web pages: sum of all pages’ ranks is one. User model: “Random surfer” selects a page, keeps clicking links (never “back”), until “bored”: then randomly selects another page and continues. PageRank(A) is the probability that such a user visits A d is the probability of getting bored at a page Google computes relevance of a page for a given search by first computing an IR relevance and then modifying that by taking into account PageRank for the top pages.
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- 33. Web Search EngineVisits
- 34. Percentage of webusers who visit the site shown
- 35. Search Engine Size(July 2000)
- 36. Does size matter? You can’t access many hits anyhow.
- 37. Increasing numbers ofindexed pages, self-reported
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- 39. From description ofthe FAST search engine, by Knut Risvik http://www.infonortics.com/searchengines/sh00/risvik_files/frame.htm
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Editor's Notes
- #2 These slides cover material from Chapter 27, but go well beyond it in depth of coverage.