公开(公告)号：US11610225B2

公开(公告)日：2023-03-21

申请号：US16854513

申请日：2020-04-21

Applicant: META PLATFORMS, INC.

Inventor： Zheng Chen ,  Shyamsundar Rajaram ,  Pradheep K. Elango

IPC: G06Q30/00 ,  G06Q30/0251 ,  G06N20/20 ,  G06Q30/0241 ,  G06Q30/02 ,  G06N5/00

Abstract: An online system optimizes for longer attribution window conversions with an additive decomposition model by predicting the probability that a predefined action happens given an impression/click. The online system receives a content item from a content provider for display to a target user, and predicts a probability that a target user will convert given an interaction with the content item by the target user. The online system computes, by a first trained model, a short-term conversion probability of a conversion event happening within a first conversion window after the interaction. The online system computes, by a second trained model, a long-term conversion probability of the a conversion event happening within a second conversion window after the interaction, the second conversion window being longer than the first conversion window. The online system computes the conversion probability given the interaction based on the short-term conversion probability and the long-term conversion probability.

公开(公告)号：US11348032B1

公开(公告)日：2022-05-31

申请号：US16120279

申请日：2018-09-02

Applicant: Meta Platforms, Inc.

Inventor： Jurgen Anne Francois Marie Van Gael ,  Yu Ning ,  Hao Shi ,  Fei Xie ,  Bingyue Peng ,  Shyamsundar Rajaram ,  Xin Liu ,  Zhen Yao ,  Peng Yang ,  Robert Oliver Burns Zeldin ,  Piyush Bansal

IPC: G06N20/00 ,  G06F8/30

Abstract: Machine-trained models are generated based on a model description that defines parameters for training the model and that can inherit parameters from parent model descriptions. When a parent model description changes, the changes made to the parent model description are applied to the model description automatically. When a target model is re-generated, a description of the set of parameters for generating the target model is received. The parent model is then identified from the received description, and a description of the set of parameters for generating the parent model is retrieved. Using the description for the target model and the parent model, a pipeline for generating the target model is generated. Finally, the pipeline is executed to generate the target model.

公开(公告)号：US20230214915A1

公开(公告)日：2023-07-06

申请号：US18113883

申请日：2023-02-24

Applicant: Meta Platforms, Inc.

Inventor： Robert Oliver Burns ZELDIN ,  Chinmay Deepak Karande ,  Shyamsundar Rajaram ,  Leon R. Cho ,  Rami Mahdi ,  Sushma Nagesh Bannur

IPC: G06Q30/08 ,  G06Q30/0601

CPC classification number: G06Q30/08 ,  G06Q30/0613

Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.

公开(公告)号：US11631125B2

公开(公告)日：2023-04-18

申请号：US15640052

申请日：2017-06-30

Applicant: META PLATFORMS, INC.

Inventor： Robert Oliver Burns Zeldin ,  Chinmay Deepak Karande ,  Shyamsundar Rajaram ,  Leon R. Cho ,  Rami Mahdi ,  Sushma Nagesh Bannur

IPC: G06Q30/08 ,  G06Q30/06 ,  G06Q30/0601

Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.