US 11,941,669 B2
Pruning for content selection
Junwei Pan, Sunnyvale, CA (US); Tian Zhou, Sunnyvale, CA (US); and Aaron Eliasib Flores, Menlo Park, CA (US)
Assigned to Yahoo Ad Tech LLC, New York, NY (US)
Filed by Yahoo Ad Tech LLC, New York, NY (US)
Filed on Apr. 24, 2023, as Appl. No. 18/138,151.
Application 18/138,151 is a continuation of application No. 17/690,938, filed on Mar. 9, 2022, granted, now 11,636,521.
Application 17/690,938 is a continuation of application No. 17/028,183, filed on Sep. 22, 2020, granted, now 11,295,346, issued on Apr. 5, 2022.
Prior Publication US 2023/0316337 A1, Oct. 5, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/0273 (2023.01); G06N 3/082 (2023.01); G06N 20/00 (2019.01)
CPC G06Q 30/0275 (2013.01) [G06N 3/082 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
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;
submitting a first bid value associated with a first content item to a first auction module for participation in a first auction associated with the first request for content;
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; 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, in real time, a machine learning model using the plurality of sets of auction information;
performing one or more pruning operations, in association with the training, to generate a first machine learning model with sparse vector representations associated with 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, using the first machine learning model, a plurality of click probabilities associated with a plurality of content items based upon one or more first sparse vector representations, of the first machine learning model, associated with the second set of features, wherein a first click probability of the plurality of click probabilities is associated with a second content item of the plurality of content items and corresponds to a probability of receiving a selection of the second content item responsive to presenting the second content item via the second client device;
selecting, from the plurality of content items, the second content item for presentation via the second client device based upon the plurality of click probabilities; and
submitting a second bid value associated with the second content item to a second auction module for participation in a second auction associated with the second request for content.