Bid value determination for a first-price auction

Patent metadata

Field	Value
Publication number	US-2022051131-A1
Application number	US-202016994976-A
Country	US
Kind code	A1
Filing date	Aug 17, 2020
Priority date	Aug 17, 2020
Publication date	Feb 17, 2022
Grant date	—

Abstract

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 impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using 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 win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

First claim

What is claimed is:

1 . A method, comprising:
receiving 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 the first shaded bid value to a first auction module for participation in a first auction associated with the first request for content; receiving a first impression indication indicative of whether the first content item is a winner of 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 impression indication; and
the first shaded bid value; 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 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; receiving 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; determining, based upon the second set of features and using the first machine learning model, a plurality of win probabilities associated with a plurality of shaded bid values, wherein:
each shaded bid value of the plurality of shaded bid values does not exceed the second bid value; and
a first win probability of the plurality of win probabilities is associated with a second shaded bid value of the plurality of shaded bid values and corresponds to a probability that the second content item wins an auction associated with the second request for content responsive to submitting the second shaded bid value to an auction module associated with the second request for content;
determining, based upon the plurality of win probabilities associated with the plurality of shaded bid values, a third shaded bid value; and submitting the third shaded bid value to a second auction module for participation in a second auction associated with the second request for content.

2 . The method of claim 1 , wherein:
the determining the plurality of win probabilities is performed based upon a set of feature parameters, of the plurality of feature parameters, associated with the second set of features.

3 . The method of claim 2 , wherein:
a first feature parameter, of the set of 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.

4 . The method of claim 3 , wherein:
the first machine learning model comprises a first bid parameter; and the determining the plurality of win probabilities is performed based upon the first bid parameter.

5 . The method of claim 4 , wherein:
the first machine learning model comprises a first bias parameter; and the determining the plurality of win probabilities is performed based upon the first bias parameter.

6 . The method of claim 5 , wherein:
the first bid parameter comprises a first bid weight; and the first bias parameter comprises a first bias weight.

7 . The method of claim 1 , wherein:
the determining the third shaded bid value comprises selecting, based upon the plurality of win probabilities associated with the plurality of shaded bid values, the third shaded bid value from the plurality of shaded bid values.

8 . The method of claim 3 , wherein:
the determining the third shaded bid value is performed based upon a set of weights, of the set of feature parameters, associated with the second set of features; and the set of weights comprises the first weight.

9 . The method of claim 5 , wherein:
the determining the third shaded bid value is performed based upon:
a set of weights, of the set of feature parameters, associated with the second set of features;
the first bid weight; and
the first bias weight; and
the set of weights comprises the first weight.

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

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

12 . 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 features comprises at least one of:
a second internet resource associated with the second request for content;
a second time of day associated with the second request for content;
a second day of week associated with the second request for content; or
a second location associated with the second client device.

13 . A computing device comprising:
a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:
receiving 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 request for content 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 the first shaded bid value to a first auction module for participation in a first auction associated with the first request for content;
receiving a first impression indication indicative of whether the first content item is a winner of 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 impression indication; and
the first shaded bid value; 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 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;
receiving 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

Assignees

• Oath Inc

Inventors

• Pan Shengjun
• Zhou Tian
• Kitts Brendan
• He Hao
• Shetty Bharatbhushan
• Gligorijevic Djordje
• Pan Junwei
• Mao Tingyu
• Gultekin San
• Paladugu Balaji Srinivasa Rao
• Zhang Jianlong
• Thomas Sneha
• Flores Aaron

Classifications

• G06N7/01

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

• G06N20/00 Primary

  Machine learning · CPC title

• G06Q30/0275 Primary

  Auctions · CPC title

• G06N5/04

  Inference or reasoning models · CPC title