Systems and Methods for Generating Recommendations from Unstructured Data Using Machine Learning Models

Abstract

Systems and methods for generating item recommendations from unstructured data using machine learning models are disclosed. One embodiment includes obtaining a dataset comprising a plurality of items, wherein each item includes at least one field containing unstructured data, preprocessing the dataset by performing filtering and text cleanup on the unstructured data, performing a coarse relatedness analysis by executing lookups on items in the dataset to identify potentially similar items and create links between items that are potentially interchangeable, performing coarse clustering by utilizing the links to organize related items into clusters using graph operations, performing fine clustering by constructing prompts for a large language model for each cluster to recluster items into subclusters and generate labels for canonical items and generating a list of interchangeable item recommendations based on the canonical items and their associated metadata.

Dependencies are inferred from the claim text.