US 10,896,380 B2Granted

Determining intent based on user interaction data

AssigneeFacebook, Inc.

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

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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).

Claims20

Dependent claims

A computer-implemented method comprising:

generating, by an online system, a plurality of features for each tracking pixel in a set of tracking pixels used by a third party system to track actions of users on the third party system, the features generated based on an analysis and labeling of:
a set of one or more content items associated with each tracking pixel, and
a set of one or more landing pages associated with each tracking pixel;
training, by the online system, a machine learning model based on the features generated about what particular action a given content item and landing page associated with each tracking pixel is directed to getting a user to take;
generating, by the online system, a plurality of features for the user based on interaction data associated with the user relating to prior interactions by the user that relate to content provided to the user that ultimately led to the user taking a previous particular action;
training, by the online system, the machine learning model based on the features generated for the user to predict an intent of the user to take a future particular action based on the user having again had interactions similar to those that ultimately led to the user taking the previous particular action;
applying, by the online system, the machine learning model to predict the intent of the user to take the future particular action and to select content items directed to getting the user to take the future particular action, wherein the intent of the user to take the future particular action is predicted based on determining a confidence score representing a likelihood that a conversion will result from the user being provided with a particular content item via the online system;
and
providing, by the online system, the particular content item of the selected content items to the user based on the intent predicted for the user.

6 more elements

A system comprising:

at least one processor;
and
a memory storing instructions that, when executed by the at least one processor, cause the system to perform:
generating a plurality of features for each tracking pixel in a set of tracking pixels used by a third party system to track actions of users on the third party system, the features generated based on an analysis and labeling of:
a set of one or more content items associated with each tracking pixel, and
a set of one or more landing pages associated with each tracking pixel;
training a machine learning model based on the features generated about what particular action a given content item and landing page associated with each tracking pixel is directed to getting a user to take;
generating a plurality of features for the user based on interaction data associated with the user relating to prior interactions by the user that relate to content provided to the user that ultimately led to the user taking a previous particular action;
training the machine learning model based on the features generated for the user to predict an intent of the user to take a future particular action based on the user having again had interactions similar to those that ultimately led to the user taking the previous particular action;
applying the machine learning model to predict the intent of the user to take the future particular action and to select content items directed to getting the user to take the future particular action, wherein the intent of the user to take the future particular action is predicted based on determining a confidence score representing a likelihood that a conversion will result from the user being provided with a particular content item via the online system;
and
providing the particular content item of the selected content items to the user based on the intent predicted for the user.

9 more elements

A computer-implemented method comprising:

generating, by an online system, a plurality of features for each tracking pixel in a set of tracking pixels used by a third party system to track actions of users on the third party system, the features generated based on an analysis and labeling of:
a set of one or more content items associated with each tracking pixel, and
a set of one or more landing pages associated with each tracking pixel;
training, by the online system, a machine learning model based on the features generated about what particular action a given content item and landing page associated with each tracking pixel is directed to getting a user to take;
generating, by the online system, a plurality of features for the user based on interaction data associated with the user relating to prior interactions by the user that relate to content provided to the user that ultimately led to the user taking a previous particular action;
training, by the online system, the machine learning model based on the features generated for the user to predict an intent of the user to take a future particular action based on the user having again had interactions similar to those that ultimately led to the user taking the previous particular action;
determining a level of specificity associated with the intent of the user to take the future particular action;
applying, by the online system, the machine learning model to predict the intent of the user to take the future particular action and to select content items directed to getting the user to take the future particular action based at least in part on the determined level of specificity;
and
providing, by the online system, a particular content item of the selected content items to the user based on the intent predicted for the user.

7 more elements

Dependencies are inferred from the claim text.