Patent ID: 11907962
Assignee: PINTEREST, INC.
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method, comprising:
capturing online behaviors of a plurality of subscribers in response to content;
generating a training dataset;
segmenting the training dataset into a plurality of batches of training data;
training a machine learning model using the plurality of batches of training data to predict conversion counts and error rates based on input online behaviors, wherein training the machine learning model includes:
training, using the plurality of batches of training data, the machine learning model by iteratively randomly disabling a processing node of the machine learning model, training the machine learning model with a respective batch of training data of the plurality of batches of training data while the processing node is disabled, and re-enabling the processing node;

for each of the plurality of subscribers, determining whether the subscriber is a measurable subscriber or a non-measurable subscriber, wherein a measurable subscriber is a subscriber whose online behaviors are accessible and can be evaluated;
for each measurable subscriber:
determining a first conversion count in response to the content; and
adding the first conversion count to a total conversion count for the content;

for each non-measurable subscriber:
predicting, using the machine learning model, a second conversion count and an estimated error rate of the second conversion count in response to the content, wherein the machine learning model is trained to compute a predicted conversion count and a predicted estimated error rate for a subscriber based at least in part on an online behavior associated with the subscriber; and
in response to determining that the estimated error rate is below a threshold, adding the second conversion count to the total conversion count; and

storing the total conversion count in conjunction with the content.