Clustering database items based on output of machine-learning model to link database items that represent the same core item but with a different stored size or form attribute

Abstract

A trained machine-learning model is used to group items in a database of an online system that represent a same core item (i.e., product) but of different attributes. The online system applies, for pairs of items from a specific chunk of the database, the machine-learning model to metadata for each pair of items and category data for each pair of items to generate a clustering score for each pair of items that indicates a likelihood that both items belong to a cluster of items that identifies the core item. The online system then requests a language model to generate a response including a list of attributes in a structured form for each item from the cluster. The online system stores, in an entry of the database associated with each item from the cluster, the list of attributes in the structured form and an identification of the cluster.