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.

公开(公告)号：US20240028933A1

公开(公告)日：2024-01-25

申请号：US17118460

申请日：2020-12-10

Applicant: Meta Platforms, Inc.

Inventor： Christian Alexander Martine ,  Robert Oliver Burns Zeldin ,  Dinkar Jain ,  Jurgen Anne Francois Marie Van Gael ,  Anand Sumatilal Bhalgat ,  Tianshi Gao

IPC: G06N7/00 ,  H04L29/08 ,  G06N20/00

CPC classification number: G06N7/005 ,  H04L67/22 ,  G06N20/00 ,  H04L67/20 ,  G06Q30/0202

Abstract: A system predicts user intent to take an action and delivers content items to the user that match that intent. A plurality of features or attributes for each tracking pixel in a set of tracking pixels can be acquired based on content items and landing pages associated with each tracking pixel. For example, features for a tracking pixel can be determined based on information associated with a content item that enabled a user to access a landing page from which the tracking pixel was fired or triggered. In this example, features for the tracking pixel can also be determined based on information associated with the landing page. The features for the tracking pixels can be utilized to train a machine learning model. The machine learning model can be trained to predict whether or not a particular user intends to produce a conversion (e.g., make a purchase).

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.