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：US20240020345A1

公开(公告)日：2024-01-18

申请号：US16017861

申请日：2018-06-25

Applicant: Meta Platforms, Inc.

Inventor： Aleksandr Ulanov ,  Dinkar Jain ,  Nikita Igorevych Lytkin ,  Apurva Jadhav ,  Yanxi Pan ,  Shike Mei

IPC: G06F17/30

CPC classification number: G06F17/30867 ,  G06F17/30554 ,  G06F17/30601

Abstract: A system uses semantic analysis of text associated with content items to recommend content for display to a user. A subset of representative words from a content description are determined and a content embedding that models the content is generated using a combination of word embeddings associated with each of the representative words. User embeddings are generated using a combination of content embeddings for content that a user has had particular interactions with in a set period of time. Separate user embeddings may be generated to represent user interactions with different categories of content (e.g., travel, photography, apparel, comedy, etc.). The system uses the content embeddings and user embeddings as input to predictive functions which determine a candidate content item that a user is likely to interact with if the candidate content is displayed to the user.