Bid value determination

Patent metadata

Field	Value
Publication number	US-11651284-B2
Application number	US-202016994930-A
Country	US
Kind code	B2
Filing date	Aug 17, 2020
Priority date	Aug 17, 2020
Publication date	May 16, 2023
Grant date	May 16, 2023

Abstract

One or more computing devices, systems, and/or methods are provided. Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of minimum bid values to win associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, unshaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using a loss function and/or the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine a shaded bid value for submission based upon one or more first feature parameters, of the feature parameters, associated with the second set of features.

First claim

What is claimed is:

1. A method, comprising:
receiving, by a demand-side platform (DSP), a first bid request, wherein:
the first bid request is associated with a first request for content associated with a first client device; and
the first bid request is indicative of a first set of features comprising one or more first features associated with the first request for content;

determining, based upon a first bid value associated with a first content item, a first shaded bid value associated with the first content item;
submitting, by the DSP, the first shaded bid value to a first auction module, of a supply-side platform (SSP), for participation in a first auction associated with the first request for content;
receiving, by the DSP, a first minimum bid to win indication from the SSP, wherein the first minimum bid to win indication corresponds to a first minimum bid value to win the first auction;
storing, in an auction information database, a first set of auction information associated with the first auction, wherein:
the first set of auction information is indicative of:
the first set of features;
the first shaded bid value; and
the first minimum bid value to win; and

the auction information database comprises a plurality of sets of auction information, comprising the first set of auction information, associated with a plurality of auctions comprising the first auction;

training a machine learning model using a first loss function and the plurality of sets of auction information to generate a first machine learning model comprising a plurality of feature parameters associated with a plurality of features of the plurality of sets of auction information, wherein:
the first loss function comprises a first value and a second value;
the first value corresponds to:
the first minimum bid value to win; or
an optimal bid reduction factor determined based upon the first minimum bid value to win and the first bid value; and

the second value corresponds to:
the first shaded bid value; or
a bid reduction factor used to determine the first shaded bid value;

loading the first machine learning model onto a bid shading module of the DSP;
receiving, by the DSP, a second bid request, wherein:
the second bid request is associated with a second request for content associated with a second client device; and
the second bid request is indicative of a second set of features comprising one or more second features associated with the second request for content;

determining a second bid value associated with a second content item;
inputting, into the bid shading module of the DSP, the second bid value and one or more first feature parameters, of the plurality of feature parameters, associated with the second set of features;
determining, using the first machine learning model loaded onto the bid shading module of the DSP, a second shaded bid value associated with the second content item based upon the second bid value and the one or more first feature parameters, of the plurality of feature parameters, associated with the second set of features; and
submitting the second shaded bid value to a second auction module for participation in a second auction associated with the second request for content,
wherein the second content item is provided for presentation on the second client device associated with the second request for content based upon a determination that the second content item is a winner of the second auction.

2. The method of claim 1 , wherein the determining the second shaded bid value comprises:
determining, based upon the one or more first feature parameters, the bid reduction factor; and
applying the bid reduction factor to the second bid value to determine the second shaded bid value.

3. The method of claim 1 , wherein:
a first feature parameter, of the one or more first feature parameters, is associated with a first feature of the second set of features; and
the first feature parameter comprises:
a first weight associated with the first feature; and
a first vector representation of the first feature.

4. The method of claim 3 , wherein the determining the second shaded bid value comprises:
determining interactions between pairs of features of the second set of features; and
combining the interactions to determine the second shaded bid value.

5. The method of claim 4 , wherein the determining the interactions comprises:
determining a first interaction between the first feature and a second feature of the second set of features based upon the first vector representation and a second vector representation, of a second feature parameter, associated with the second feature.

6. The method of claim 1 , comprising:
generating, using the first loss function, a plurality of loss values associated with the plurality of sets of auction information, wherein:
a first loss value of the plurality of loss values is associated with the first set of auction information associated with the first auction; and
the generating the plurality of loss values comprises generating the first loss value based upon a difference between the first value and the second value.

7. The method of claim 6 , wherein:
the determining the first shaded bid value comprises:
determining, based upon one or more feature parameters associated with the first set of features, the bid reduction factor; and
applying the bid reduction factor to the first bid value to determine the first shaded bid value; and

the second value corresponds to the bid reduction factor, the method comprising:
determining the optimal bid reduction factor associated with the first auction based upon the first minimum bid value to win and the first bid value, wherein the first value to win corresponds to the optimal bid reduction factor.

8. The method of claim 7 , wherein:
the first loss value is greater if the optimal bid reduction factor exceeds the bid reduction factor by a first difference than if the optimal bid reduction factor is less than the bid reduction factor by the first difference.

9. The method of claim 6 , wherein:
the first value corresponds to the first minimum bid value; and
the second value corresponds to the first shaded bid value.

10. The method of claim 9 , wherein:
the first loss value is greater if the first shaded bid value is less than the first minimum bid value by a first difference than if the first shaded bid value exceeds the first minimum bid value by the first difference.

11. The method of claim 6 , wherein:
the generating the first loss value is performed based upon a difference between the first bid value and the first minimum bid value.

12. The method of claim 6 , wherein:
the training the machine learning model to generate the first machine learning model is performed based upon the plurality of loss values.

13. The method of claim 1 , wherein:
the second auction is a first-price auction.

14. The method of claim 1 , wherein:
the first shaded bid value is less than the first bid value; and
the second shaded bid value is less than the second bid value.

15. The method of claim 1 , wherein:
the first auction module is the same as the second auction module.

16. The method of claim 1 , wherein:
the first set of features comprises at least one of:
a first internet resource associated with the first request for content;
a first time of day associated with the first request for content;
a first day of week associated with the first request for content; or
a first location associated with the first client device; and

the second set of fea

Assignees

• Oath Inc
• Yahoo Ad Tech Llc

Inventors

• Zhou Tian
• Gligorijevic Djordje
• Shetty Bharatbhushan
• Pan Junwei
• Kitts Brendan
• Pan Shengjun
• Paladugu Balaji Srinivasa Rao
• Thomas Sneha
• Flores Aaron

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06Q30/0275 Primary

  Auctions · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title