Adobe, Inc.

Alok Kothari

Saayan Mitra

Somdeb Sarkhel

Jiatong Xie

Viswanathan Swaminathan

Gang Wu

Michael Schiff

Seth Olson

Chih Hsin Hsueh

Venkata Karthik Penikalapati

Daniel Pang

Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.

Artificial intelligence techniques for bid optimization used for generating dynamic online content

The present disclosure relates to systems, non-transitory computer-readable media, and methods to generate sketches for clearing-bid values and bid-success rates based on multi-dimensional targeting criteria for a digital-content campaign and dynamically determine predicted values for the digital-content campaign based on the sketches. To illustrate, the disclosed systems can use a running-average-tuple-sketch to generate tuple sketches of historical clearing-bid values and tuple sketches of historical bid-success-rates from historical auction data. Based on the tuple sketches, the disclosed systems can determine one or more of a predicted cost per quantity of impressions, a predicted number of impressions, or a predicted expenditure for the digital-content campaign&#x2014;according to user-input targeting criteria and expenditure constraints.

Utilizing a sketching generator to adaptively generate content-campaign predictions for multi-dimensional or high-dimensional targeting criteria

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San Francisco, CA, United States of America

Alok Kothari