Google Inc.

Patrick Hummel

Randolph Preston McAfee

Christopher Kenneth Harris

Hal Ronald Varian

Aranyak Mehta

Qian Zhong

Timothy S Lipus

Gagan Aggarwal

Shaohua Sun

Uri Nadav

Kshipra Uday Bhawalkar

Joshua V Dillon

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing decision systems. In some implementations, methods can include receiving a component request specifying a maximum number of digital components that are capable of being presented on a particular electronic document being rendered at a client device. A ranking score for the given digital component is determined based on a maximum interaction increase factor of formatting available to be applied to the given digital component, the current eligibility value, and the historical eligibility value data. The given digital component is ranked among other available digital components based on the determined ranking score. The given digital component is selected for distribution based on the ranking score of the given digital component being included in the maximum number of highest ranked digital components. The given digital component is transmitted to a client device.

Optimized digital component analysis system

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium. In some implementations, methods include determining multiple different configurations of multiple digital components eligible to be presented within a single digital component slot in a given electronic document being presented at a client device, selecting a particular configuration that has a highest configuration score and includes a given digital component, determining an offset factor that quantifies a negative impact of the given digital component on other digital components, optimizing a distribution amount applied to the given digital component based on the offset factor and a baseline distribution amount applied to each digital component in each of the multiple different configurations, and transmitting the given digital component to the client device based on the distribution amount for the given digital component, including causing the given digital component to be presented at the client device in the particular configuration.

The subject matter of this specification can be embodied in, among other things, a method that includes receiving a request for content to fill a block, the request including information about a size of the block and criteria for identifying eligible content items for inclusion in the block, determining a number of content items to be included in the block, including determining amounts of dynamic resizing to apply to content items, determining an estimate of a difference in conversion rates conditional on receiving an interaction in the different configurations that perform differing amounts of dynamic resizing, and applying the estimate when determining an efficiency for a number of permutations of allocations of eligible content items, establishing a price to be charged to each content sponsor associated with a permutation that has a highest efficiency, and providing the eligible content items and charging the content sponsors based on the established prices.

Choosing allocations and prices in position auctions

Systems and methods of the present disclosure can provide an improved user experience on client devices selecting to receive context-based third-party content by reducing the number of third-party content items presented for display on such client devices. A data processing system can be configured to employ an auction mechanism with selection criterion thresholds (such as minimum bid values) that result, when used in auctions, in enforcing a limit on the amount of third-party content presented for display on client devices that have selected to receive context-based third-party content. The selection criterion thresholds can result, when used in auctions, in enforcing minimum revenues for publishers. The selection criterion thresholds can be determined offline based on historical statistical parameters.

System and method for providing context-based third-party content

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing content items are disclosed. In one aspect, a method includes accessing a scaling factor value and accessing a first page value range specifying at least a high page value and a low page value. A determination is made that a first ranking of content items based on the high page value does not match a second ranking of the content items that is based on the low page value. In response to determining that the first ranking does not match the second ranking, an updated first ranking and an updated second ranking are determined based on a second page value range. A determination is made that the updated first ranking matches the updated second ranking. Content items are distributed based on the updated first ranking.

Performance based content item ranking

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for ranking content items. In one aspect, a method includes identifying, for a content item, a bid value specifying an amount a content item provider is willing to pay for user interaction with the content item. A predicted performance measure is identified for the content item. The predicted performance measure is adjusted based on a weighting factor for the content item. The weighting factor for the content item is indicative of confidence that the predicted performance measure will match an actual performance measure for the content item and can be different than a weighting factor for another content item identified for inclusion in a ranking with the content item. A rank score is determined for the content item using the bid value and adjusted predicted performance measure. The content item is provided based on the rank score.

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Cupertino, CA, United States of America

Patrick Hummel