Oath Inc.

Zhihui Xie

Kuang-Chih Lee

Junwei Pan

Pradip Thachile

The present teaching relates to dynamically generate a score floor model that provides a dynamically determined threshold metric to be used to select future bids that satisfy a first and second set of metrics. Feedback information is first received that relates to a plurality of bids previously submitted by a bidder to a publisher, wherein the feedback information indicates whether each of the plurality of bids has led to an impression. The first set of metrics specified by the publisher is obtained and the second set of metrics is retrieved. The score floor model with respect to the publisher is dynamically updated based on the feedback information as well as the first and second sets of metrics.

Method and system for dynamic score floor modeling and application thereof

Systems, devices, and methods are disclosed for predicting a dynamic floor price for increasing cleared revenue cleared after a winning bid is determined in an online bid auction. The dynamic floor price is predicted from a cascading classifier strategy implemented through a series of cascading machine learning based classifier models that have been trained.

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Sunnyvale, CA, United States of America

Zhihui Xie