Airbnb, Inc.

Twitter, Inc.

Benjamin Henry Yolken

Li Zhang

Yangli Hector Yee

Peng Ye

Spencer De Mars

Kim Pham

Fenglin Liao

Julian Qian

In general, embodiments of the invention relate to servicing fill request using ad networks and demand-side partners.

Ordering of fill requests to send to online ad networks to fill an ad unit in an electronic publisher application with an online advertising impression

This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.

Regression-tree compressed feature vector machine for time-expiring inventory utilization prediction

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San Francisco, CA, United States of America

Benjamin Henry Yolken