Dstillery, Inc.

Jason Jerard Kaufman

Melinda Han Williams

Amelia Grieve White

Christopher Allen Jenness

Mark Alan Jung

Evan Bard Hills

Justin Matthew Moynihan

Alexander Michael Kokkoros

Navarre E Pratt

Some embodiments described herein relate to computer implemented methods and/or memory (e.g., a non-transitory, processor-readable medium) storing code configured to be executed by a processor to cause the processor to perform the method. The method can include training a first transformer with a plurality of website visitation records to predict a missing website in a sequence of websites. The first transformer can include a foundation model and a first prediction head. A second transformer model can be trained with a plurality of conversion event data to predict a probability of a conversion event occurring. The second transformer model can include the foundation model and a second prediction head.

Transformers adapted for predictive targeting based on web browsing history

A method includes accessing web browsing history for a plurality of users, generating embedding vectors based on the web browsing history for websites, and selecting a model configured to receive embedding vectors and output probability of a conversion events. Further, the method includes calculating a probability of a conversion event for the various websites using the model, selecting a subset of websites from the various websites based on websites having associated probabilities greater that a predetermined probability threshold, and receiving an indication that an impression has been displayed to a user when the user visits a website from the subset of websites, obtaining a plurality of conversion rates, each conversion rate is determined for each website from the subset of websites based on a number of conversion events associated with the plurality of visitation events, and updating the model parameters of the model using the obtained plurality of conversion rates.

Iterative online learning to improve targeted advertising

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.

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

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White Plains, NY, United States of America

Jason Jerard Kaufman