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

A eigenvector and time technology, applied in the field of eigenvector machine output, can solve problems such as list pricing difficulties

Active Publication Date: 2019-01-04
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This combination of factors makes it difficult for managers of time-expiring lists to optimally price their lists

Method used

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

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] I. System overview

[0026] figure 1 is a block diagram of a computing environment including an online system for providing time-expired listings for purchases, leases, leases, reservations, etc., according to one embodiment. Network 109 represents a communication path between one party to a transaction (referred to herein as client 102A) and the other party to the transaction (referred to herein as administrator 102B).

[0027] For clarity of explanation, client includes potential purchasers for value, lessors, lessees, clients holding reservations, or any other party that offers consideration in exchange for access to time-expired listings in whatever form. For clarity of explanation, managers include sellers of items on time-expired listings, landlords and property owners who manage property on behalf of landlords, lessors, those who manage reserved listings (such as ticket salespeople or restaurant or hotel reservation personnel) or Any other party that receives ...

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PUM

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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 togenerate 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 toexpiration used to generate the demand estimate that was used to generate the request likelihood.

Description

[0001] Cross References to Related Applications [0002] This application claims the benefit of U.S. Provisional Application No. 62 / 325,006, filed April 20, 2016, and U.S. Application No. 15 / 482,453, filed April 7, 2017, the entire contents of which are incorporated herein by reference. technical field [0003] The present application relates generally to eigenvector machine learning models and data analysis in the context of time-expiring checklists, and in particular to the application of regression trees to eigenvector machine outputs for exploit predictions related to time-expiring checklists. Background technique [0004] In a typical transaction for a good or service, a manager who controls or owns the good sets the price of the good and waits for interested parties to agree to pay the proposed price. Oftentimes, regulators fail to price commodities correctly, but due to imperfect information in the market and other economic factors, someone may eventually agree to pa...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06N20/00
CPCG06Q30/0206G06Q10/067G06Q10/087G06N20/20G06N20/00G06N5/01G06F17/18G06Q10/04G06Q10/0637G06N5/04
Inventor S·德马尔斯Y·H·叶叶鹏廖枫林张力K·潘J·钱B·约尔肯
Owner AIRBNB
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