Abstract: Users can create natural language prompts that describe a content moderation policy. A computing system can then process these prompts using a machine-learned multi-modal embedding generation model to create policy embeddings. Then, for an item of content to be screened for the content moderation policy, the system can also process the item of content with the machine-learned multi-modal embedding generation model to create one or more content embeddings. The system can then compare the policy embedding(s) with the content embedding(s) to determine whether or not the content violates the content moderation policy.

Abstract: The technology is generally directed to personalizing digital components for users based on a merged preference profile. The merged preference profile may be generated based on declared and inferred preferences of the users. The declared and inferred preferences may be preferences related to brands, topics, types of products, etc. The merged preference profile may be used to personalize the digital components output to the user. For example, when identifying digital components to be selected, at least some of the digital components may be selected based on the merged preference profile of the user. At least a portion of the selected digital components may be digital components with a subject matching the preferences of the user, thereby improving the digital components output to the user.