14 resultados para Google

em Boston University Digital Common


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http://books.google.com/books?vid=ISBN0665456816&id=sipohllLjKQC&dq=protestant+missions&a_sbrr=1 View book via Google

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http://books.google.com/books?vid=OCLC00289131

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http://books.google.com/books?vid=ISBN0665456816&id=sipohllLjKQC&dq=protestant+missions&a_sbrr=1 View book via Google

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http://books.google.com/books?vid=OCLC05060511&id=B1cMAAAAIAAJ&a_sbrr=1 via Google Book Index

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$u http://books.google.com/books?vid=OCLC02623863&id=mQz8gPn0et8C&a_sbrr=1 View book via Google

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http://books.google.com/books?vid=OCLC11413587 View volume 1

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Digitized by Google

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http://books.google.com/books?id=plhkPFrJ1QUC&dq=law+and+custom+of+slavery+in+British+India

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http://books.google.com/books?vid=OCLC09108077

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http://books.google.com/books?vid=OCLC00615969

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The Google AdSense Program is a successful internet advertisement program where Google places contextual adverts on third-party websites and shares the resulting revenue with each publisher. Advertisers have budgets and bid on ad slots while publishers set reserve prices for the ad slots on their websites. Following previous modelling efforts, we model the program as a two-sided market with advertisers on one side and publishers on the other. We show a reduction from the Generalised Assignment Problem (GAP) to the problem of computing the revenue maximising allocation and pricing of publisher slots under a first-price auction. GAP is APX-hard but a (1-1/e) approximation is known. We compute truthful and revenue-maximizing prices and allocation of ad slots to advertisers under a second-price auction. The auctioneer's revenue is within (1-1/e) second-price optimal.

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Attributing a dollar value to a keyword is an essential part of running any profitable search engine advertising campaign. When an advertiser has complete control over the interaction with and monetization of each user arriving on a given keyword, the value of that term can be accurately tracked. However, in many instances, the advertiser may monetize arrivals indirectly through one or more third parties. In such cases, it is typical for the third party to provide only coarse-grained reporting: rather than report each monetization event, users are aggregated into larger channels and the third party reports aggregate information such as total daily revenue for each channel. Examples of third parties that use channels include Amazon and Google AdSense. In such scenarios, the number of channels is generally much smaller than the number of keywords whose value per click (VPC) we wish to learn. However, the advertiser has flexibility as to how to assign keywords to channels over time. We introduce the channelization problem: how do we adaptively assign keywords to channels over the course of multiple days to quickly obtain accurate VPC estimates of all keywords? We relate this problem to classical results in weighing design, devise new adaptive algorithms for this problem, and quantify the performance of these algorithms experimentally. Our results demonstrate that adaptive weighing designs that exploit statistics of term frequency, variability in VPCs across keywords, and flexible channel assignments over time provide the best estimators of keyword VPCs.