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Information retrieval and web search ppt

internet search engines ppt and web browser and search engine ppt and web search basics information retrieval ppt
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RyanCanon,United Arab Emirates,Teacher
Published Date:20-07-2017
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Introduction to Information Retrieval Introduction to Information Retrieval Web search basics www.ThesisScientist.comIntroduction to Information Retrieval Brief (non-technical) history  Early keyword-based engines ca. 1995-1997  Altavista, Excite, Infoseek, Inktomi, Lycos  Paid search ranking: Goto (morphed into  Yahoo)  Your search ranking depended on how much you paid  Auction for keywords: casino was expensive www.ThesisScientist.comIntroduction to Information Retrieval Brief (non-technical) history  1998+: Link-based ranking pioneered by Google  Blew away all early engines save Inktomi  Great user experience in search of a business model  Meanwhile Goto/Overture’s annual revenues were nearing 1 billion  Result: Google added paid search “ads” to the side, independent of search results  Yahoo followed suit, acquiring Overture (for paid placement) and Inktomi (for search)  2005+: Google gains search share, dominating in Europe and very strong in North America  2009: Yahoo and Microsoft propose combined paid search offering www.ThesisScientist.comIntroduction to Information Retrieval Paid Search Ads Algorithmic results. www.ThesisScientist.comIntroduction to Information Retrieval Sec. 19.4.1 Web search basics Sponsored Links CG Appliance Express Discount Appliances (650) 756-3931 Same Day Certified Installation San Francisco-Oakland-San Jose, CA User Miele Vacuum Cleaners Miele Vacuums- Complete Selection Free Shipping Miele Vacuum Cleaners Miele-Free Air shipping All models. Helpful advice. Results 1 - 10 of about 7,310,000 for miele. (0.12 seconds) Web Miele, Inc Anything else is a compromise At the heart of your home, Appliances by Miele. ... USA. to Residential Appliances. Vacuum Cleaners. Dishwashers. Cooking Appliances. Steam Oven. Coffee System ... - 20k - Cached - Similar pages Miele Welcome to Miele, the home of the very best appliances and kitchens in the world. - 3k - Cached - Similar pages Web spider Miele - Deutscher Hersteller von Einbaugeräten, Hausgeräten ... - Translate this page Das Portal zum Thema Essen & Geniessen online unter Miele weltweit ...ein Leben lang. ... Wählen Sie die Miele Vertretung Ihres Landes. - 10k - Cached - Similar pages Herzlich willkommen bei Miele Österreich - Translate this page Herzlich willkommen bei Miele Österreich Wenn Sie nicht automatisch weitergeleitet werden, klicken Sie bitte hier HAUSHALTSGERÄTE ... - 3k - Cached - Similar pages Search Indexer The Web Indexes Ad indexesIntroduction to Information Retrieval Sec. 19.4.1 User Needs  Need Brod02, RL04  Informational – want to learn about something (40% / 65%) Low hemoglobin  Navigational – want to go to that page (25% / 15%) United Airlines  Transactional – want to do something (web-mediated) (35% / 20%)  Access a service Seattle weather Mars surface images  Downloads Canon S410  Shop  Gray areas Car rental Brasil  Find a good hub  Exploratory search “see what’s there” www.ThesisScientist.comIntroduction to Information Retrieval How far do people look for results? (Source: WhitePaper_2006_SearchEngineUserBehavior.pdf) www.ThesisScientist.comIntroduction to Information Retrieval Users’ empirical evaluation of results  Quality of pages varies widely  Relevance is not enough  Other desirable qualities (non IR)  Content: Trustworthy, diverse, non-duplicated, 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 www.ThesisScientist.comIntroduction to Information Retrieval Users’ empirical evaluation of engines  Relevance and validity of results  UI – Simple, no clutter, error tolerant  Trust – Results are objective  Coverage of topics for polysemic queries  Pre/Post process tools provided  Mitigate user errors (auto spell check, search assist,…)  Explicit: Search within results, more like this, refine ...  Anticipative: related searches  Deal with idiosyncrasies  Web specific vocabulary  Impact on stemming, spell-check, etc.  Web addresses typed in the search box  “The first, the last, the best and the worst …” www.ThesisScientist.comIntroduction to Information Retrieval Sec. 19.2 The Web document collection  No design/co-ordination  Distributed content creation, linking, democratization of publishing  Content includes truth, lies, obsolete information, contradictions …  Unstructured (text, html, …), semi- structured (XML, annotated photos), structured (Databases)…  Scale much larger than previous text collections … but corporate records are catching up  Growth – slowed down from initial “volume doubling every few months” but The Web still expanding  Content can be dynamically generated www.ThesisScientist.comIntroduction to Information Retrieval SPAM (SEARCH ENGINE OPTIMIZATION) www.ThesisScientist.comIntroduction to Information Retrieval Sec. 19.2.2 The trouble with paid search ads …  It costs money. What’s the alternative?  Search Engine Optimization:  “Tuning” your web page to rank highly in the algorithmic 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 www.ThesisScientist.comIntroduction to Information Retrieval Sec. 19.2.2 Search engine optimization (Spam)  Motives  Commercial, political, religious, lobbies  Promotion funded by advertising budget  Operators  Contractors (Search Engine Optimizers) for lobbies, companies  Web masters  Hosting services  Forums  E.g., Web master world ( )  Search engine specific tricks  Discussions about academic papers  www.ThesisScientist.comIntroduction to Information Retrieval Sec. 19.2.2 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 Pure word density cannot be trusted as an IR signal www.ThesisScientist.comIntroduction to Information Retrieval Sec. 19.2.2 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, …” www.ThesisScientist.comIntroduction to Information Retrieval Sec. 19.2.2 Cloaking  Serve fake content to search engine spider  DNS cloaking: Switch IP address. Impersonate SPAM N Is this a Search Engine spider? Real Y Cloaking Doc www.ThesisScientist.comIntroduction to Information Retrieval Sec. 19.2.2 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 www.ThesisScientist.comIntroduction to Information Retrieval The war against spam  Quality signals - Prefer  Spam recognition by authoritative pages based machine learning  Training set based on known on: spam  Votes from authors (linkage  Family friendly filters signals)  Linguistic analysis, general  Votes from users (usage signals) classification techniques, etc.  Policing of URL submissions  For images: flesh tone detectors, source text analysis,  Anti robot test etc.  Limits on meta-keywords  Editorial intervention  Robust link analysis  Blacklists  Ignore statistically implausible  Top queries audited linkage (or text)  Complaints addressed  Use link analysis to detect  Suspect pattern detection spammers (guilt by association) www.ThesisScientist.comIntroduction to Information Retrieval More on spam  Web search engines have policies on SEO practices they tolerate/block    Adversarial IR: the unending (technical) battle between SEO’s and web search engines  Research www.ThesisScientist.comIntroduction to Information Retrieval SIZE OF THE WEB