Robust Taxonomy out of Receipt Item Labels

The primary objective of this project is to implement a product taxonomy model that can reliably categorize receipt item labels to generate more personalized financial insights. Sensibill leverages unstructured receipt data to support personal and business finance management. Reliable categorization of receipt line items into specific merchant categories not only reduces the time for manual entry, but also helps customers better understand their spending patterns, as well as support banks and credit unions in providing individualized recommendations that are aligned with customers’ financial goals. Considering the continuously expanding and often closely related sets of product categories, a hierarchical product taxonomy that leverages the relationship – from general to specific – amongst receipt item categories would power downstream features, including more fine-grained categorization of historical purchases and delivery of more personalized financial insights. To achieve such results, we will implement and evaluate natural language processing and machine learning approaches for hierarchical product categorization.

Faculty Supervisor:

Michael Guerzhoy;Rohan Alexander

Student:

Partner:

Sensibill Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program: