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Enregistrement W308199711

Webnotes: Is Google Getting Too Good?

2007· article· en· W308199711 sur OpenAlex
Bill Orr

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
Aucune affiliation canadienne. Une base fondée sur la seule affiliation (le devis habituel) n'aurait jamais vu ce travail. C'est l'un des travaux qui justifient l'inversion de la base.

Notice bibliographique

RevueABA banking journal · 2007
Typearticle
Langueen
DomaineComputer Science
ThématiqueInformation Retrieval and Search Behavior
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWorld Wide WebWeb pageSentenceSearch engine optimizationComputer scienceThe InternetArtificial intelligence
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

mission is to organize the world's information and make it universally accessible and useful That's the first sentence their website's Corporate Overview. mean it. And by any measure they're getting there. A metered study by Nielsen/NetRatings July 2006 measured the web search behavior of 500,000 people worldwide and found that 49.2% of their searches were done by Google. Runners up were Yahoo (23.8%), Microsoft (9.6%), AOL (6.3%--they use Google's search engine), and Ask.com (2.6%). A year Later, Netapplications gave Google 55% of the worldwide searches, when Google UK and Google Canada were included. The googling of the web is a story as revolutionary as the emergence of the web as a commercial tool. Amazingly it was just nine years ago that two Stanford Ph.D. candidates Larry Page and Sergey Brin, incorporated their Silicon Valley garage operation, whimsically naming it for the mathematical term googol, ten followed by 100 zeros. The name reflected their unique approach to search--look at every word of every page the whole World Wide Web, not just the webpage title. Their trick for doing that was to use as many parallel processors as it would take. (Google won't say how many processors it uses today, but published estimates range from 175,000 to in excess of 450,000.) About these arrays of computers at data centers around the world, one observer huffed: They are so primitive I wouldn't give one of them to my son for his high school work. But they do the job. That is, first, to crawl through every webpage and capture its contents. Then the computers compile an index of all the elements. In 2001 Google patented PageRank, their unique system for judging how closely each found website matches the search query. The system delivers to the searcher a List, with snippets of contents, of dozens, hundreds, tens of thousands, or millions of websites that best match the search query. Then it ranks them the order of their likely match to what the user intended. To determine what most closely matches the user's intention, the system analyzes the words and content of each page, using an algorithm with more than 500 million variables and two billion terms. PageRank weighs the vote of each page's intrinsic relevance, apart from the specific words the search query. The whole process is automatic and normally takes less than half a second. The strategy has obviously paid off big time. Its second quarter report showed $3.87 billion revenues, up 58% from the previous year; and $1.22 billion operating income (29% of revenues, 4% Lower than first quarter 2007). The firm had $12.5 billion cash on hand as of June 30, 2007, and 13,786 employees. Where did all that money come from? A big share of profits comes from advertising. Two closely meshed programs, called AdWords and AdSense, drive Google's ad revenues. The program's goal is to attract leads and turn them into sales at the Lowest cost. For some time, advertisers have known that digital advertising does this better than alternative marketing media. In his 2006 book The Search, John Battelle cites these customer-acquisition figures from Piper Jaffray: $8.50 per customer with search; $20 with yellow pages; $50 through online display ads; $60 with e-mail; and $70 with direct mail. Using AdWords, an advertiser creates text ads for placement throughout the web where they will Likely attract the most qualified prospects. Those digital ads are worded to motivate that prospect--then Looking at another webpage--to click on the Google-enabled ad there and be transported to the advertiser's webpage. What magic words have the best chance of doing that? Google answers: put words the ad that match the search words or the content of the page where the prospect went an unrelated web search. The advertiser can put one ad as many such words as she wants to. Since Google knows all the words all the webpages, it's a good position to suggest the matching words--or even a whole text ad. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,934
Score d'incertitude au seuil0,605

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,282
Écart entre enseignants0,262 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle