Artificial intelligence and/or machine learning models trained to predict user actions based on an embedding of network locations

Abstract

A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.

What is claimed is: 
     
         1 . A computer-implemented method, comprising:
 accessing a first plurality of website visitation records, each website visitation record from the first plurality website visitation records associated with a user device from a first plurality of user devices that has not opted out of having website visitation tracked;   generating a p-dimensional embedding of a plurality of websites based on the first plurality website visitation records;   accessing a plurality of conversion event data, the plurality of conversion event data associated with a second plurality website visitation records, the plurality of conversion event data indicating that a subset of the second plurality of user devices performed a conversion action, each website visitation record from the second plurality of website visitation records associated with a user device from a second plurality of user devices and indicating a plurality of websites visited by that user device;   determining, for each website visitation record from the second plurality of website visitation records, a position of each website from the plurality of websites indicated in that website visitation record in the p-dimensional embedding;   training a machine learning model, using the plurality of conversion event data and the position of each website from the plurality of websites indicated in each website visitation record from the plurality of website visitation records, to predict a likelihood of conversion given a position in the p-dimensional embedding;   receiving an indication that an untracked user device is accessing a website;   determining the position of the website in the p-dimensional embedding; and   facilitating delivery of targeted content to the untracked user device based on predicting, using the machine learning model, a likelihood of whether the untracked user device will perform a conversion action based on the position of the website in the p-dimensional embedding.