Abstract: Methods, systems, and apparatuses for forecasting pricing for an advertisement campaign that covers a set of impressions. A dynamic cost per mille (dCPM) value is defined for the campaign. An effective bid price is determined for each of the impressions to generate a plurality of effective bid prices. A parametric distribution is determined based on the plurality of effective bid prices. A mean and a variance are determined for the determined parametric distribution. Bids for the impressions are determined based on the effective bid prices and a participation probability function. The bids are determined in a manner that is independent of an order in which the bids are processed.

Abstract: A method, advertising network, and computer readable medium for forecasting ad traffic based on business metrics in performance-based display advertising. The method commences by defining a set of advertising campaign parameters, the advertising campaign parameters comprising target predicates, a campaign pricing model, and a campaign performance model. The method continues by forecasting a supply of impressions satisfying target predicates, based on a statistical analysis of a historical dataset containing impressions satisfying target predicates. Once a measure of forecasted supply is known, an auction model serves for calculating the likelihood of winning the forecasted impression at auction, based the campaign pricing model and the campaign performance model. Having a forecasted supply, and also an assessment of the likelihood of winning at auction, the method proceeds by determining values for various performance metrics.

Abstract: A system and method for recommending targeting attributes for advertising campaigns are provided. The system and method comprise receiving at least one advertizing campaign, receiving historical targeting data and historical click-through data, selecting a machine learning model, and determining recommended targeting attributes for the advertising campaign using the machine learning model and the received data. The machine learning model may be a collaborative filtering model or a performance oriented model. The collaborative filtering model may rely on matrix factorization or Boltzmann Machines. The performance-oriented model may rely on a local regression.

Abstract: A contextual advertising system optimizes computer selection of low performance ranked messages and high performance ranked messages for display on a network location. The system divides a ranked group of online messages into a first list, a second list, and a promotion set. Each message in the first list has a performance score that is greater than each performance score of messages in the second list and the promotion set. The system moves a message within the promotion set to a third list as a function of a confidence value and moves a message from one of the third list and the second list to the first list based on an experiment event outcome. The system transmits top messages in the first list over a network for display at a recipient computer.

Abstract: A system for selecting advertisements for a web page. An advertisement request is received and the web page content is evaluated to identify a first advertisement query based on the content of the web page and a second advertisement query based on the content of the web page or a behavioral feature. A first set of advertisements is retrieved based on the first advertisement query and a second set of advertisements is retrieved based on the second advertisement query. The first set of advertisements is blended with the second set of advertisements to form a blended advertisement set. The blended advertisement set is provided for display with the web page.