Searching the Web

1. Searching the Web From Chris Manning’s slides for Lecture 8: IIR Chapter 19

2. Basic assumptions of Classic Information Retrieval • Corpus: Fixed document collection • Goal: Retrieve documents with content that is relevant to user’s information need

3. Bad assumptions in the web context Classic IR Goal • Classic relevance • For each query Q and stored document D in a given corpus assume there exists relevance Score(Q, D) • Score is average over users U and contexts C • Optimize Score(Q, D) as opposed to Score(Q, D, U, C) • That is, usually: • Context ignored • Individuals ignored • Corpus predetermined

4. Advertisements =Monetization Algorithmic results =Audience

5. Brief (non-technical) history • Early keyword-based engines • Altavista, Excite, Infoseek, Inktomi, Lycos, ca. 1995-1997 • Sponsored search ranking: Goto.com (morphed into Overture.com  Yahoo!) • Your search ranking depended on how much you paid • Auction for keywords: casino was expensive!

6. Brief (non-technical) history • 1998+: Link-based ranking pioneered by Google • Blew away all early engines except Inktomi • Great user experience in search of a business model • Meanwhile Goto/Overture’s annual revenues were nearing $1 billion • Result: Google added paid-placement “ads” to the side, independent of search results • 2003: Yahoo follows suit, acquiring Overture (for paid placement) and Inktomi (for search)

7. User Web spider Search Indexer The Web Indexes Ad indexes Web search basics

8. Web search engine pieces • Spider (a.k.a. crawler/robot) – builds corpus • Collects web pages recursively • For each known URL, fetch the page, parse it, and extract new URLs • Repeat • Additional pages from direct submissions & other sources • The indexer – creates inverted indexes • Various policies wrt which words are indexed, capitalization, support for Unicode, stemming, support for phrases, etc. • Query processor – serves query results • Front end – query reformulation, word stemming, capitalization, optimization of Booleans, etc. • Back end – finds matching documents and ranks them

9. The Web The Web • No design/co-ordination • Distributed content creation, linking • Content includes truth, lies, obsolete information, contradictions … • Structured (databases), semi-structured … • Scale larger than previous text corpora … (now, corporate records) • Growth – slowed down from initial “volume doubling every few months” • Content can be dynamically generated

10. The user • Diverse in access methodology • Increasingly, high bandwidth connectivity • Growing segment of mobile users: limitations of form factor – keyboard, display • Diverse in search methodology • Search, search + browse, filter by attribute … • Average query length ~ 2.3 terms • Has to do with what they’re searching for • Poor comprehension of syntax • Early engines surfaced rich syntax – Boolean, phrase, etc. • Current engines hide these

11. The user • Diverse in background/training • Although this is improving • Few try using the CD ROM drive as a cupholder • Increasingly, can tell a search bar from the URL bar • Although this matters less now • Increasingly, comprehend UI elements such as the vertical slider • But browser real estate “above the fold” is still a premium

12. Mendocino weather Mars surface images Nikon CoolPix The user: information needs • Informational – want to learn about something (~25%) • Navigational – want to go to that page (~40%) • Transactional – want to do something (web-mediated) (~35%) • Access a service • Downloads • Shop • Gray areas • Find a good hub • Exploratory search “see what’s there” Low hemoglobin United Airlines Car rental Finland

13. Users’ empirical evaluation of results • Quality of pages varies widely • Relevance is not enough • Other desirable qualities (non IR!!) • Content: Trustworthy, new info, non-duplicates, well maintained, • Web readability: display correctly & fast • No annoyances: pop-ups, etc • Precision vs. recall • On the web, recall seldom matters • What matters • Precision at 1? Precision above the fold? • Comprehensiveness – must be able to deal with obscure queries • Recall matters when the number of matches is very small • User perceptions may be unscientific, but are significant over a large aggregate

14. Algorithmic results • Already, more than a list of docs • Moving towards identifying a user’s task • (As expressed in the query box) • Instead: enabling means for task completion

18. Sponsored search

19. A sponsored search ad

20. Ads go in slots like these

21. Higher slots get more clicks

22. IR Econ Engine: Three sub-problems • Match ads to query/context • Order the ads • Pricing on a click-through

23. Spam Search Engine Optimization

24. The trouble with search ads … • It costs money. What’s the alternative? • Search Engine Optimization: • “Tuning” your web page to rank highly in the search results for select keywords • Alternative to paying for placement • Thus, intrinsically a marketing function • Performed by companies, webmasters and consultants (“Search engine optimizers”) for their clients • Some perfectly legitimate, some very shady

25. The spam industry

26. Simplest forms • First generation engines relied heavily on tf/idf • The top-ranked pages for the query maui resort were the ones containing the most maui’s and resort’s • SEOs responded with dense repetitions of chosen terms • e.g., mauiresort maui resort maui resort • Often, the repetitions would be in the same color as the background of the web page • Repeated terms got indexed by crawlers • But not visible to humans on browsers Term density is not a trusted IR signal

27. Variants of keyword stuffing • Misleading meta-tags, excessive repetition • Hidden text with colors, style sheet tricks, etc. Meta-Tags = “… London hotels, hotel, holiday inn, hilton, discount, booking, reservation, sex, mp3, britney spears, viagra, …”

28. SPAM Y Is this a Search Engine spider? Real Doc N Cloaking • Serve fake content to search engine spider • DNS cloaking: Switch IP address. Impersonate Cloaking

30. More spam techniques • Doorway pages • Pages optimized for a single keyword that re-direct to the real target page • Link spamming • Mutual admiration societies, hidden links, awards – more on these later • Domain flooding: numerous domains that point or re-direct to a target page • Robots • Fake query stream – rank checking programs • “Curve-fit” ranking programs of search engines • Millions of submissions via Add-Url

31. Quality signals - Prefer authoritative pages based on: Votes from authors (linkage signals) Votes from users (usage signals) Policing of URL submissions Anti robot test Limits on meta-keywords Robust link analysis Ignore statistically implausible linkage (or text) Use link analysis to detect spammers (guilt by association) Spam recognition by machine learning Training set based on known spam Family friendly filters Linguistic analysis, general classification techniques, etc. For images: flesh tone detectors, source text analysis, etc. Editorial intervention Blacklists Top queries audited Complaints addressed Suspect pattern detection The war against spam

32. More on spam • Web search engines have policies on SEO practices they tolerate/block • http://help.yahoo.com/help/us/ysearch/index.html • http://www.google.com/intl/en/webmasters/ • Adversarial IR: the unending (technical) battle between SEO’s and web search engines • Research http://airweb.cse.lehigh.edu/

33. Size of the web

34. What is the size of the web ? • Issues • The web is really infinite • Dynamic content, e.g., calendar • Soft 404: www.yahoo.com/anything is a valid page • Static web contains syntactic duplication, mostly due to mirroring (~20-30%) • Some servers are seldom connected • Who cares? • Media, and consequently the user • Engine design • Engine crawl policy. Impact on recall

35. The relative size of search engines The notion of a page being indexed is stillreasonably well defined. Already there are problems Document extension: e.g. engines indexe pages not yet crawled, by indexing anchortext. Document restriction: Some engines restrict what is indexed (first n words, only relevant words, etc.) The coverage of a search engine relative to another particular crawling process. What can we attempt to measure?

36. IQ is whatever the IQ tests measure. The statically indexable web is whatever search engines index. Different engines have different preferences max url depth, max count/host, anti-spam rules, priority rules, etc. Different engines index different things under the same URL: frames, meta-keywords, document restrictions, document extensions, ... New definition ?

37. Sampling URLs • Ideal strategy: Generate a random URL and check for containment in each index. • Problem: Random URLs are hard to find! Enough to generate a random URL contained in a given Engine. Key lesson.

38. Sampling methods • Random queries • Random searches • Random IP addresses • Random walks

39. AÇB Relative Size from Overlap [Bharat & Broder, 98] Sample URLs randomly from A Check if contained in B and vice versa AÇ B= (1/2) * Size A AÇ B= (1/6) * Size B (1/2)*Size A = (1/6)*Size B \ Size A / Size B = (1/6)/(1/2) = 1/3 Each test involves: (i) Sampling (ii) Checking

40. Random URLs from random queries [Bharat & B, 98] • Generate random query • Lexicon:400,000+ words from a crawl of Yahoo! • Conjunctive Queries: w1 and w2 e.g., vocalists AND rsi • Get 100 result URLs from the source engine • Choose a random URL as the candidate to check for presence in other engines. • This distribution induces a probability weight W(p) for each page. • Conjecture: W(SE1) / W(SE2) ~= |SE1| / |SE2|

41. Query Based Checking • “Strong Query” for sample document: • Download document. Get list of words. • Use 8 low frequency words as conjunctive query • Check if sample is present in result set. • Problems: • Near duplicates • Frames • Redirects • Engine time-outs • Might be better to use e.g. 5 distinct conjunctive queries of 6 words each. • Document extensions • Document restrictions … • Sequence of improvements to Bar-Youssef & Gurevich 2006

42. Random searches • Choose random searches extracted from a local log [Lawrence & Giles 97] or build “random searches” [Notess] • Use only queries with small results sets. • Count normalized URLs in result sets. • Use ratio statistics • Some particularly flawed implementations also exist

43. Random searches [Lawr98, Lawr99] • 575 & 1050 queries from the NEC RI employee logs • 6 Engines in 1998, 11 in 1999 • Implementation: • Restricted to queries with < 600 results in total • Counted URLs from each engine after verifying query match • Computed size ratio & overlap for individual queries • Estimated index size ratio & overlap by averaging over all queries • Issues • Samples are correlated with source of log • Duplicates • Technical statistical problems (must have non-zero results, ratio average, use harmonic mean? )

44. adaptive access control neighborhood preservation topographic hamiltonian structures right linear grammar pulse width modulation neural unbalanced prior probabilities ranked assignment method internet explorer favourites importing karvel thornber zili liu softmax activation function bose multidimensional system theory gamma mlp dvi2pdf john oliensis rieke spikes exploring neural video watermarking counterpropagation network fat shattering dimension abelson amorphous computing Queries from Lawrence and Giles study

45. Random IP addresses [Lawrence & Giles ‘99] • Generate random IP addresses • Find, if possible, a web server at the given address • Collect all pages from server. • Method first used by O’Neill, McClain, & Lavoie, “A Methodology for Sampling the World Wide Web”, 1977. http://digitalarchive.oclc.org/da/ViewObject.jsp?objid=0000003447

46. Random IP addresses [ONei97, Lawr99] • HTTP requests to random IP addresses • Ignored: empty or authorization required or excluded • [Lawr99] Estimated 2.8 million IP addresses running crawlable web servers (16 million total) from observing 2500 servers. • OCLC using IP sampling found 8.7 M hosts in 2001 • Netcraft [Netc02] accessed 37.2 million hosts in July 2002 • [Lawr99] exhaustively crawled 2500 servers and extrapolated • Estimated size of the web to be 800 million • Estimated use of metadata descriptors: • Meta tags (keywords, description) in 34% of home pages, Dublin core metadata in 0.3%

47. Advantages & disadvantages • Advantages • Clean statistics • Independent of crawling strategies • Disadvantages • Doesn’t deal with duplication • Many hosts might share one IP or not accept http://102.93.22.15 • No guarantee all pages are linked to root page. • Power law for # pages/host generates bias towards sites with few pages. • But bias can be accurately quantified IF underlying distribution understood • Potentially influenced by spamming (multiple IP’s for same server to avoid IP block)

48. Random walks [Henzinger & al.,‘99 & WWW9] • View the Web as a directed graph from a given list of seeds. • Build a random walk on this graph • Includes various “jump” rules back to visited sites • Does not get stuck in spider traps! • Can follow all links! • Converges to a stationary distribution • Must assume graph is finite and independent of the walk. • Conditions are not satisfied (cookie crumbs, flooding) • Time to convergence not really known • Sample from stationary distribution of walk • Use the “strong query” method to check coverage by SE • Related papers: • [Bar Yossef & al, VLDB 2000], [Rusmevichientong & al, 2001], [Bar Yossef & al, 2003]

49. Advantages & disadvantages • Advantages • “Statistically clean” method at least in theory! • Could work even for infinite web (assuming convergence) under certain metrics. • Disadvantages • List of seeds is a problem. • Practical approximation might not be valid. • Non-uniform distribution • Subject to link spamming • Still has all the problems associated with “strong queries”

50. Conclusions • No sampling solution is perfect. • Lots of new ideas ... • ....but the problem is getting harder • Quantitative studies are fascinating and a good research problem