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

US10528909B2 · kind B2 · utility

Assignee

• Airbnb, Inc. · US

Inventors

• Spencer de Mars · San Francisco, US
• Yangli Hector Yee · San Francisco, US
• Peng Ye · Chengdu, CN
• Fenglin Liao · San Francisco, US
• Li Zhang · San Diego, US
• Kim Pham · San Francisco, US
• Julian Qian · San Francisco, US
• Benjamin Yolken · San Francisco, US

Key dates

• Filing date: Apr 7, 2017
• Grant date: Jan 7, 2020
• Expiry date: Apr 7, 2037

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/0206
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

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.