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1 Introduc)on to Informa(on Retrieval CS276 Informa)on Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan Lecture 15: Web search basics Introduc)on to Informa)on Retrieval Brief (non‐technical) history Early keyword‐based engines ca. 1995‐1997 Altavista, Excite, Infoseek, Inktomi, Lycos Paid search ranking: Goto (morphed into Overture.com Yahoo!) Your search ranking depended on how much you paid Auc)on for keywords: casino was expensive! 2 Introduc)on to Informa)on 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, domina)ng in Europe and very strong in North America 2009: Yahoo! and Microso_ propose combined paid search offering 3 Introduc)on to Informa)on Retrieval Algorithmic results. Paid Search Ads 4 Introduc)on to Informa)on Retrieval Web search basics The Web Ad indexes Web spider Indexer Indexes Search User Sec. 19.4.1 5 Introduc)on to Informa)on Retrieval User Needs Need [Brod02, RL04] Informa(onal – want to learn about something (~40% / 65%) Naviga(onal – want to go to that page (~25% / 15%) Transac(onal – want to do something (web‐mediated) (~35% / 20%) Access a service Downloads Shop Gray areas Find a good hub Exploratory search “see what’s there” Sec. 19.4.1 6
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2 Introduc)on to Informa)on Retrieval How far do people look for results? (Source: iprospect.com WhitePaper_2006_SearchEngineUserBehavior.pdf) 7 Introduc)on to Informa)on Retrieval Users’ empirical evalua)on of results Quality of pages varies widely Relevance is not enough Other desirable quali)es (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 mapers What mapers Precision at 1? Precision above the fold? Comprehensiveness – must be able to deal with obscure queries Recall mapers when the number of matches is very small User percep)ons may be unscien)fic, but are significant over a large aggregate 8 Introduc)on to Informa)on Retrieval Users’ empirical evalua)on of engines Relevance and validity of results UI – Simple, no cluper, error tolerant Trust – Results are objec)ve Coverage of topics for polysemic queries Pre/Post process tools provided Mi)gate user errors (auto spell check, search assist,…) Explicit: Search within results, more like this, refine ...
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