1. Web Search Tutorial Jan Pedersen and Knut Magne Risvik Yahoo! Inc. Search and Marketplace

2. Agenda A Short History Internet Search Fundamentals –Web Pages –Indexing Ranking and Evaluation Third Generation Technologies

3. A Short History

4. Precursors Information Retrieval (IR) Systems –online catalogs, and News Limited scale, homogeneous text –recall focus –empirical Driven by results on evaluation collections –free text queries shown to win over Boolean Specialized Internet access –Gopher, Wais, Archie FTP archives and special databases Never achieved critical mass

5. First Generation Systems 1993: Mosaic opens the WWW –1993 Architext/Excite (Stanford/Kleiner Perkins) –1994 Webcrawler (full text Indexing) –1994 Yahoo! (human edited Directory) –1994 Lycos (400K indexed pages) –1994 Infoseek (subscription service) Power systems –1994 AltaVista (Dec Labs, advanced query syntax, large index) –1996 Inktomi (massively distributed solution)

6. Second Generation Systems Relevance matters –1998 Direct Hit (clickthrough based re-ranking) –1998 Google (link authority based re-ranking) Size matters –1999 FAST/AllTheWeb (scalable architecture) The user matters –1996 Ask Jeeves (question answering) Money matters –1997 Goto/Overture (pay-for-performance search)

7. Third Generation Systems Market consolidation –2002 Yahoo! Purchases Inktomi –2003 Overture purchases AV and FAST/AllTheWeb –2003 MSN announces intention to build a Search Engine Search matures –$2B market projected to grow to $6B by 2005 –required capital investment limits new players Gigablast? –traffic focused in a few sites Yahoo!, MSN, Google, AOL –consumer use driven by Brand marketing

8. Web Search Fundamentals

9. Web Fundamentals URL User BrowserWeb Server HTML Page Page RenderingPage Serving Hyper Links HTTP Request

10. Definitions URL’s refer to WWW content –referential integrity is not guaranteed –roughly 10% of Url’s go 404 every month HTTP requests fetch content from a server –stateless protocol –cookies provide partial state Web servers generate HTML pages –can be static or dynamic (output of a program) –markup tags determine page rendering HTML pages contain hyperlinks –link consists of a url and anchor text

11. Url’s URL Definition –http://host:port/path;params?query#fragmenthttp://host:port/path;params?query#fragment fragment is not considered part of the URL params are considered part of the path –params are not frequently used Examples –http://www.cnn.com/http://www.cnn.com/ –http://ad.doubleclick.net/jump;sz=120x60;ptile=6;ord=69810 62172http://ad.doubleclick.net/jump;sz=120x60;ptile=6;ord=69810 62172 –http://us.imdb.com/Title?0068646http://us.imdb.com/Title?0068646 –http://www.sky.com/skynews/article/0,,30000- 12261027,00.htmlhttp://www.sky.com/skynews/article/0,,30000- 12261027,00.html

12. Dynamic Url’s Urls with Dynamic Components –Path (including params) and host are not dynamic If you change the PATH and/or host you will get a 404 or similar error –Query is dynamic If you change the query part, you will get a valid page back source of potentially infinite number of pages Examples –http://www.cnn.com/index.html?testhttp://www.cnn.com/index.html?test Returns a valid 200 page, even if test is not a valid query term –http://www.cnn.com/index.html;testhttp://www.cnn.com/index.html;test Returns a 404 error page Not all Url’s Follow this Convention: –http://www.internetnews.com/xSP/article.php/1378731http://www.internetnews.com/xSP/article.php/1378731

13. Dynamic Content Content Depends on External (to URL) Factors –Cookies –IP –Referrer –User-Agent Examples –http://my.yahoo.com/http://my.yahoo.com/ –http://forum.doom9.org/forumdisplay.php?s=af9ddb31710c7 b314b75262c1031d8af&forumid=65http://forum.doom9.org/forumdisplay.php?s=af9ddb31710c7 b314b75262c1031d8af&forumid=65 Dynamic Url’s and Dynamic Content are Orthogonal –static url’s can refer to dynamic content –dynamic url’s can refer to static content

14. HMTL Sample Andreas S. WEIGEND, PhD Andreas S. WEIGEND, Ph.D. Chief Scientist, Amazon.com "Sophisticated algorithms have always been a big part of creating the Amazon.com customer experience." (Jeff Bezos, Founder and CEO of Amazon.com) Amazon.com might be the world's largest laboratory to study human behavior and decision making. It for sure is a place with very smart people, with a healthy attitude towards data, measurement, and modeling. I am responsible for research in machine learning and computational marketing. Applications range from real-time predictions of customer intent and satisfaction, to personalization and long-term optimization of pricing and promotions. [<a href="http://www.weigend.com/amazonjobs.html" onclick="window.open(this.href);return false;">Job openings. ] I'm also the point person for academic relations. Schedule Summer 2003

15. Rendered Page

16. WWW Size How pages are in the WWW? –Lawrence and Giles, 1999: 800M pages with most pages not indexed –Dynamically generated pages imply effective size is infinite How many sites are registered? –Churn due to SPAM

17. Crawling Search Engine robot –visits every page that will be indexed –traversal behavior depends on crawl policy Index parameterized by size and freshness –freshness is time since last revisit if page has changed Batch vs Incremental –Batch crawl has several, distinct, batch processing stages discover, grab, index AV discovery phase takes 10 days, grab another 10, etc. sharp freshness curve –Incremental crawl crawler constantly operates, intermixing discovery with grab mild drop-off in freshness

18. Typical Crawl/Build Architecture Grab URL DB Seed List Discovery Internet Pagefiles Filtered Pagefiles Index Pagefiles Anchor Text DB Connectivity DB Duplicates DB Alias DB Index Build Crawl

19. Relative Size From SearchEngineShowdown Google claims 3B Fast claims 2.5B AV claims 1B

20. Freshness From Search Engine Showdown Note hybrid indices; subindices with differing update rates

21. Query Language Free text with implicit AND and implicit proximity –Syntax-free input Explicit Boolean –AND (+) –OR (|) –AND NOT (-) Explicit Phrasing (“”) Filters –domain:filetype: –host:title: –link:image: –url:anchor:

22. Query Serving Architecture Index divided into segments each served by a node Each row of nodes replicated for query load Query integrator distributes query and merges results Front end creates a HTML page with the query results Load Balancer FE 1 QI 1 Node 1,1 Node 1,2 Node 1,3 Node 1,N Node 2,1 Node 2,2 Node 2,3 Node 2,N Node 4,1 Node 4,2 Node 4,3 Node 4,N Node 3,1 Node 3,2 Node 3,3 Node 3,N QI 2 QI 8 FE 2 FE 8 “travel” … … … … … … … …

23. Query Evaluation Index has two tables: –term to posting –document ID to document data Postings record term occurrences –may include positions Ranking employs posting –to score documents Display employs document info –fetched for top scoring documents Terms  Posting Doc ID  Doc Data Query Evaluator “travel” rankingdisplay

24. Scale Indices typically cover billions of pages –terrabytes of data Tens of millions of queries served every day –translates to hundreds of queries per second User require rapid response –query must be evaluated in under 300 msecs Data Centers typically employ thousands of machines –Individual component failures are common

25. Search Results Page Blended results –multiple sources Relevance ranked Assisted search –Spell correction Specialized indices –via Tabs Sponsored listing –monetization Localization –Country language experience

26. Relevance Evaluation

27. Relevance is Everything The Search Paradigm: 2.4 words, a few clicks, and you’re done –only possible if results are very relevant Relevance is ‘speed’ –time from task initiation to resolution –important factors: Location of useful result UI Clutter latency Relevance is relative –context dependent e.g. ‘football’ in the UK vs the US –task dependent e.g. ‘mafia’ when shopping vs researching

28. Relevance is Hard to Measure Poorly defined, subjective notion –depends on task, user context, etc. Analysts have Focused on Easier-to-Measure Surrogates –index size, traffic, speed –anecdotal relevance tests e.g. Vanity queries Requires Survey Methodology –averaged over queries –averaged over users

29. Survey Methodologies Internal expert assessments –assessments typically not replicated –models absolute notion of relevance External consumer assessments –assessments heavily replicated –models statistical notion of relevance A/B surveys –compare whole result sets –visual relevance plays a large role Url surveys –judge relevance of particular url for query

30. A/B Test Design Strategy: –Compare two ranking algorithms by asking panelists to compare pairs of search results Queries: –1000 semi-random queries, filtered for family-friendly, understandability Users can select from a list of 20 queries URLS –Top 10 search results from 2 algorithms Voting: –5 point scale, 7 replications –Each user rates 6 queries, one of which is a control query Control query has AV results on one side, random URLs on the other Reject voters who take less than 10 seconds to vote

31. Query selection screen

32. Rating screen

33. A/B Test Scoring Test ran until we had 400 decisive votes –Margin of error = 5% Compute: –Majority Vote: count of queries where more than half of the users said one engine was “somewhat better” or “much better” –Total Vote: count of users that rated a result set “somewhat better” of “better” for each engine Compare percentages –test if one system ‘out votes’ the other –determine if the difference is statistically significant

34. Results Queries with winnerAll accepted votes MajorityUnanimous“a little better”“much better” AltaVista37.6%6.1%24.4%11.0% SE137.3%8.0%22.2%10.7% Same25.1%2.6%31.7% Queries with winnerAll accepted votes MajorityUnanimous“a little better”“much better” Good98.1%51.5%24.4%59.1% Bad 0% 4.7%1.7% Same1.9%0.6%10.1% Control Votes (error bar = 1/sqrt(160) = 7.9%) Test One: AV vs SE1 (error bar = 1/sqrt(400) = 5%)

35. Results Queries with winnerAll accepted votes MajorityUnanimous“a little better”“much better” AltaVista58.5%13.4%26.5%16.3% SE228.1%4.6%21.8%8.9% Same13.4%0.9%26.4% Queries with winnerAll accepted votes MajorityUnanimous“a little better”“much better” SE135.4%4.7%28.2%13.2% SE240.6%4.1%29.0%15.6% Same24.0%1.9%13.8% Test Three: SE1 Vs SE2 Test Two: AV Vs SE2 (with UI issue)

36. Ranking Given 2.4 query terms, search 2B documents and return 10 highly relevant in 300 msecs –Problem queries: Travel (matches 32M documents) John Ellis (which one) Cobra (medical or animal) Query types –Navigational (known item retrieval) –Informational Ingredients –Keyword match (title, abstract, body) –Anchor Text (referring text) –Quality (link connectivity) –User Feedback (clickrate analysis)

37. The Components of Relevance First Generation: –Keyword matching Title and abstract worth more Second Generation: –Computed document authority Based on link analysis –Anchor text matching Webmaster voting Development Cycle: Tune Ranking Evaluate Metrics

38. Connectivity

39. Connectivity Goals An indicator of authority –As measured by static links –Each link is a ‘vote’ in favor of a site –Webmasters are the voters Not all links are equal –Links from authoritative sites are worth more Introduces an interesting circularity –Votes from sites with many links are discounted Use your vote wisely –Discount navigational links Not all links are editorial –Account for link SPAM

40. Connectivity Network A B What is authority score for nodes A and B? Inlink computes: –A = 3 –B = 2 Page Rank Computes –A =.225 –B =.295

41. Definitions Connectivity Graph –Nodes are pages (or hosts) –Directed edges are links –Graph edges can be represented as a transition matrix, A The ith row of A represents the links out from node i Authority score –Score associated with each node –Some function of inlinks to node and outlinks from node Simplest authority score is inlink count

42. Contribution averaged over all outlinks Node score is the sum of contributions Fixed point equation –If A is normalized Each row sums to 1.0 Page Rank (Without Random Jump).1 A (.25) B (.3) 1/2.1

43. A is a stochastic matrix –r(i) can be interpreted as a probability Suppose a surfer takes a outlink at random r(i) is the long run probability of landing at a particular node –Solution to fixed point equation is the principal Eigen vector principal Eigen value is 1.0 Solution can be found by iteration –If then –Start with random initial value for r –Iterate multiplication by A Contribution of smaller eigen values will drop out –Final value is a good estimate of the fixed point solution Page Rank Implications

44. What’s the score for a node with no in-links? Revised equation Fixed point equation Probability interpretation –As before with  chance of jumping randomly Page Rank (with random jump).1 A (.225) B (.293) 1/2.1  = 0.1

45. Eigenrank Separates internal from external links –Internal transition matrix I –External transition matrix E Introduces a new parameter –  is the random jump probability –  is the probability of taking an internal link –(1 -  -  ) is the probability of taking an external link

46. Revised equation Fixed point equation Probability interpretation –  chance of random jump –  chance of internal link –(1-  -  ) chance of external link Eigenrank.1 A (.2) B (.202) 1/2.1  = 0.1  = 0.1

47. Computational Issues Nodes with no outlinks –Transition matrix with zero row Internal or external –Leave out of computation(?) –Redistribute mass to random jump(?) Currently mass is redistributed –Complex formula that prefers external links

48. Two scores –Authority score, a –Hub score, h Fixed Point equations –Authority –Hub –Principal Eigen vectors are solutions Kleinberg

49. SPAM Manipulation of content purely to influence ranking –Dictionary SPAM –Link sharing –Domain hi-jacking –Link farms Robotic use of search results –Meta-search engines –Search Engine optimizers –Fraud

50. Third Generation Technologies

51. Handling Ambiguity Results for query: Cobra

52. Impression Tracking Incoherent urls are those that receive high rank for a large diversity of queries. Many incoherent urls indicate SPAM or a bug (as in this case).

53. Clickrate Relevance Metric Average highest rank clicked perceptibly increased with the release of a new rank function.

54. User Interface Ranked result lists –Document summaries are critical Hit highlighting Dynamic abstracts url –No recent innovation Graphical presentations not well fit to the task Blending –Predefined segmentation e.g. Paid listing –Intermixed with results from other sources e.g. News

55. Future Trends Question Answering –WWW as language model Enables simple methods e.g. Dumais et al. (SIGIR 2002) New contexts –Ubiquitous Searching Toolbars, desktop, phone –Implicit Searching Computed links New Tasks –E.g. Local/ Country Search

56. Bibliography Modeling the Internet and the Web: Probabilistic Methods and Algorithms by Pierre Baldi, Paolo Frasconi, and Padhraic Smyth John Wiley & Sons; May 28, 2003 Mining the Web: Analysis of Hypertext and Semi Structured Data by Soumen Chakrabarti Morgan Kaufmann; August 15, 2002 The Anatomy of a Large-scale Hypertextual Web Search Engine by S. Brin and L. Page. 7th International WWW Conference, Brisbane, Australia; April 1998. Websites: –http://www.searchenginewatch.com/ –http://www.searchengineshowdown.com/http://www.searchengineshowdown.com/ Presentations –http://infonortics.com/searchengines/sh03/slides/evans.pdf