DYNAMIC FLOOR VALUE OPTIMIZATION USING MACHINE LEARNING MODELS

원문 (중국어)

A computer-implemented method comprising collecting bid data corresponding to bids of different user computers in a real-time computer-implemented auction, wherein each user computer is associated with a digital content item that a content server can serve to a web server; based on bid data parameters of the bid data, aggregating digital identifiers of user computers into a plurality of user groups, and digitally storing a training dataset comprising the bid data parameters and the user groups; training a machine learning model (MLM) using the training dataset to create and store a trained MLM; executing an inference stage of the trained MLM over input data comprising characteristics of a particular user group from among the plurality of user groups and revenue-per-thousand impressions data (RPM data) to output a prediction of a group-associated floor bid value for the particular user group; transmitting, to the content server, the floor bid value and an identifier of the particular user group; and dynamically adjusting, by the content server, a frequency of updating the floor bid value, wherein the adjusting occurs when an average maximum bid price for a set of auctions exceeds the floor bid value.