Facebook, Inc.

John Hegeman

Rong Yan

Alon Amit

Maxim Gubin

Yanxin Shi

Hong Ge

Andrey Goder

David Dawei Ye

Yaron Greif

An advertising selection and placement system is provided for a social networking system. An advertising selection module identifies candidate advertisements for a user to view along with social networking content. The candidate advertisements can be placed in various slots on the user's display. The expected value of various arrangements of the candidate advertisements in the slots is determined, and advertisements may be selected and placed to optimize revenue to the system. Each advertisement is evaluated using a discount function that adjusts the price of the advertisement based on its placement.

Advertisement selection and pricing using discounts based on placement

A social networking system presents advertisements and recommendation units to its users. The recommendation units suggest actions for the users to increase their engagement with the social networking system or otherwise interact with other users, while the social networking system receives revenue from advertisers for displaying advertisements based on bid values associated with the advertisements. The social networking system determines values for the advertisements and for the recommendation units, where the values are measured in a comparable fashion. This allows the system to rank and select the advertisements and recommendation units together in a unified auction model. For example, the social networking system uses a pacing value to determine values of recommendation units having a common unit of measurement with expected values of advertisements to the social networking system.

Unified auction model for suggesting recommendation units and ad units

A social networking system (SNS) provides sponsored stories and organic stories about actions taken by other SNS users to a viewing user. Organic stories are selected based on the likelihood the viewing user is interested in their content. While advertisers compensate the SNS for presentation of sponsored stories, the sponsored stories also include information about actions by other SNS users. To increase the likelihood the viewing user interacts with sponsored stories, a common communication channel is used to present both the sponsored stories and the organic stories. To simplify selection of organic stories and sponsored stories, the SNS determines a common unit of measurement for both and makes selections based on the common unit of measurement.

Sponsored advertisement ranking and pricing in a social networking system

A social networking system presents content items to users, who then provide feedback regarding pairs of content items. The feedback includes a selection of a content item of the pair of content items that was preferred by the user over the other content item. The social networking system uses this information to train a predictive model that scores content items based on quality. The content items may be advertisements. The social networking system uses the pair-wise comparisons of the advertisements to determine feedback coefficients in an advertising quality score prediction model using regression analysis of the pair-wise comparisons for each predictive factor in the model. In this way, the pair-wise comparisons are used to train the prediction model to understand which advertisements are more enjoyable than others. A feedback coefficient for each predictive factor may be computed based on the preferences received from the group of users.

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Palo Alto, CA, United States of America

John Hegeman