Solution Accelerator
Fuzzy Item Matching
Pre-built code, sample data and step-by-step instructions ready to go in a Databricks Notebook
Fuzzy item matching is an essential function in many retail and consumer goods organizations. Whether it's comparing new product offerings to ones already offered on a vast online marketplace to minimize seller redundancy, the scraping of competitor information on a website for price comparisons, supplier verification of online listings to ensure terms and conditions for sale are being met, or the harmonization of retailer and market analyst data with internal product hierarchies, many organizations spend a tremendous amount of time and energy identifying product matches between data sets in order to enable their work.
Optimize product matching to drive sales
Use machine learning and the Databricks Lakehouse Platform for product matching that can be utilized by marketplaces and suppliers for various purposes. Resolve differences between product definitions and descriptions and determine which items are likely pairs and which are distinct across disparate data sets.
Use images and metadata for fuzzy matching with Zingg
With this Solution Accelerator, you can perform product matching with product metadata and images. Identify duplicates and matches between an initial set of product data and an incremental set of "newly arrived" product data and convert thumbnail images to embeddings leveraging a general-purpose image model from Hugging Face.
Resources
Blog
Machine Learning-Based Item Matching for Retailers and Brands
Blog
Retail in the Age of Generative AI
Blog
