Product Information Management (PIM) is a critical process in the retail industry to manage product data, such as descriptions, images, and other attributes. In this blog post, we will show how to use Large Language Models (LLMs) with Vertex AI to enrich product data, which can improve the customer experience and the bottom line.
Product Information Management
PIM is the process of collecting, storing, and managing product information across an organization. It includes gathering data from a variety of sources, such as product catalogs, websites, and customer feedback. PIM systems then organize and normalize this data so that it can be used by other systems, such as e-commerce platforms, marketing automation tools, and product recommendations engines. The PIM market is growing rapidly, as businesses increasingly recognize the importance of having accurate and up-to-date product information.
LLMs can support the PIM process in a number of ways, including:
- Generating product descriptions: LLMs can be trained on a large corpus of product descriptions to generate new descriptions for products.
- Translating product descriptions: LLMs can be used to translate product descriptions into multiple languages.
- Extracting product attributes: LLMs can be used to extract product attributes from product descriptions, such as the product name, price, and features.
Getting started
For this demonstration, we’ll use the Flipkart products dataset on Kaggle. It provides a sample of 20,000 products from the Indian e-commerce retailer Flipkart, with 15 fields including name, description, and price.
Our goal will be to improve the quality of product descriptions in the dataset. In particular, let’s look for short or incomplete descriptions that can be augmented.
You can follow along with the Colab notebook. Here, we will highlight key steps but not include all of the details.
Data analysis
Our first step will be to understand the distribution of product descriptions. We can create a Kernel Density Estimation (KDE) plot, which can help us visualize a smoothed distribution of the data.
