Customized pairing recommendations by machine-learning language learning models

A non-transitory computer-readable storage medium storing instructions that, when executed by a computer processor system, cause the computer processor system to perform operations comprising:

• obtaining an anchor item that a user of an online system interacted with;
• mapping the anchor item to an anchor category describing a category the anchor item is assigned to;
• generating a prompt for a machine learning language model, the prompt specifying a request for a set of beverage categories based on the anchor category and for each beverage category, a respective pairing score indicating a degree of relevance between the anchor category and the beverage category and a reasoning for the pairing of the beverage category;
• receiving, as an output from the machine learning language model, the set of candidate beverage categories and the pairing score for each beverage category and the reasoning for the beverage category;
• obtaining one or more beverage items from a catalog database that is assigned to one or more beverage categories in the set of beverage categories;
• selecting a subset of the one or more beverage items;
• and
• providing the subset of beverage items to a client device of the user to cause presentation of the subset of beverage items on a page generated on the client device.

A computer system, comprising:

• a computer processor system;
• and
• a non-transitory computer-readable storage medium storing instructions that, when executed by a computer processor system, cause the computer processor system to perform operations comprising:
• obtaining an anchor item that a user of an online system interacted with;
• mapping the anchor item to an anchor category describing a category the anchor item is assigned to;
• generating a prompt for a machine learning language model, the prompt specifying a request for a set of beverage categories based on the anchor category and for each beverage category, a respective pairing score indicating a degree of relevance between the anchor category and the beverage category and a reasoning for the pairing of the beverage category;
• receiving, as an output from the machine learning language model, the set of candidate beverage categories and the pairing score for each beverage category and the reasoning for the beverage category;
• obtaining one or more beverage items from a catalog database that is assigned to one or more beverage categories in the set of beverage categories;
• selecting a subset of the one or more beverage items;
• and
• providing the subset of beverage items to a client device of the user to cause presentation of the subset of beverage items on a page generated on the client device.