Abstract: A system includes a computing platform including processing hardware and a memory storing software code, a trained machine learning (ML) model, and a content thumbnail generator. The processing hardware executes the software code to receive interaction data describing interactions by a user with content thumbnails, identify, using the interaction data, an affinity by the user for at least one content thumbnail feature, and determine, using the interaction data, a predetermined business rule, or both, content for promotion to the user. The software code further provides a prediction, using the trained ML model and based on the affinity by the user, of the desirability of each of multiple candidate thumbnails for the content to the user, generates, using the content thumbnail generator and based on the prediction, a thumbnail having features of one or more of the candidate thumbnails, and displays the thumbnail to promote the content to the user.

Abstract: Various embodiments set forth systems and techniques for personalizing censorship of digital content. The techniques include generating, based on a user characteristic, a censorship profile associated with a user, wherein the censorship profile identifies a censorship element; determining a censorship feature associated with the censorship element, wherein the censorship feature is to be activated during playback of digital content that includes the censorship element; identifying, based on temporal boundaries data and temporal facet data associated with a digital content item, one or more portions of the digital content item that include at least one element matching the censorship element; and activating the censorship feature during playback of the one or more portions of the digital content item.