Incremental model training for advertisement targeting using real-time streaming data and model redistribution

摘要

Incremental model training for advertisement targeting is performed using streaming data. A model for targeting advertisements of an advertising campaign is initialized. A data stream including data corresponding to converters and data corresponding to non-converters is received. The model is then applied to the data corresponding to the converter and data corresponding to the non-converter (or other ratio of converter to non-converters) to obtain a predicted score for each. The predicted score is compared to the observed score (e.g., an observed score of 1 for a converter, and 0 for a non-converter). The difference between the predicted and observed scores is computed, and the model is incrementally updated based on this difference. Models can optionally be built separately on multiple modeling servers that are geographically dispersed in order to support bidding on advertising opportunities in a real-time bidding environment.

主权项

1. A computer-implemented method of incrementally training a model for targeting advertisements, the method comprising:
initializing a model at a first modeling server; receiving a real-time data stream of data corresponding to converters and data corresponding to non-converters at the first modeling server; responsive to receiving a signal of conversion corresponding to a single converter, pairing the converter and a randomly sampled non-converter and performing for the single converter and the non-converter:
applying the model to data corresponding to the converter to obtain a predicted score for the converter;applying the model to data corresponding to the non-converter to obtain a predicted score for the non-converter;comparing the predicted score for the converter to an observed score for the converter to compute a first difference;comparing the predicted score for the non-converter to an observed score for the non-converter to compute a second difference;updating the model at the first modeling server based on the first and second differences;periodically combining the updated model with at least one other model from at least a second modeling server; andredistributing the combined model to at least the first and second modeling servers.