Using a generative artificial intelligence model to generate a personalized image of a catalog item

A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising:

• retrieving a set of user data for a user of an online system;
• accessing a machine-learning model trained to predict a measure of preference of the user associated with an item category, wherein the machine-learning model is trained by:
• receiving user data for a plurality of users of the online system,
• receiving, for each user of the plurality of users, a label describing the measure of preference of a corresponding user associated with the item category, and
• training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users;
• applying the machine-learning model to predict the measure of preference of the user associated with the item category based at least in part on the set of user data for the user;
• for an item included in the item category, generating a prompt that requests generation of an image of the item that is consistent with the predicted measure of preference of the user associated with the item category;
• providing the prompt to a generative artificial intelligence model to obtain an output;
• extracting, from the output, the image of the item that is consistent with the predicted measure of preference of the user associated with the item category;
• updating a database of items by storing the extracted image of the item in a database record associated with the item;
• receiving a future request by a different user that will result in a display of the item;
• determining, from stored data relating to the different user, that the different user is part of a user cohort to which the user belongs, wherein the different user shares the predicted measure of preference;
• and
• retrieving for display, in association with the future request, the image.

A computer system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising:
• retrieving a set of user data for a user of an online system;
• accessing a machine-learning model trained to predict a measure of preference of the user associated with an item category, wherein the machine-learning model is trained by:
• receiving user data for a plurality of users of the online system,
• receiving, for each user of the plurality of users, a label describing the measure of preference of a corresponding user associated with the item category, and
• training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users;
• applying the machine-learning model to predict the measure of preference of the user associated with the item category based at least in part on the set of user data for the user;
• for an item included in the item category, generating a prompt that requests generation of an image of the item that is consistent with the predicted measure of preference of the user associated with the item category;
• providing the prompt to a generative artificial intelligence model to obtain an output;
• extracting, from the output, the image of the item that is consistent with the predicted measure of preference of the user associated with the item category;
• updating a database of items by storing the extracted image of the item in a database record associated with the item;
• receiving a future request by a different user that will result in a display of the item;
• determining, from stored data relating to the different user, that the different user is part of a user cohort to which the user belongs, wherein the different user shares the predicted measure of preference;
• and
• retrieving for display, in association with the future request, the image.