Retail / E-commerce
Amazon: Standardising product attributes and correcting errors
- Company
- Amazon
- Country
- United States
- Adoption stage
- In operation
- Source published
- Date basis
- The date the source was published. It can differ from the date adoption started.
- How the source was checked
- Checked a source excerpt through a search tool
The work problem
The Amazon catalogue holds hundreds of millions of products, and millions of product records are added or edited every day. The company had kept quality up with machine learning models built separately for each product category. That approach fitted products described by size, shape, colour and material, such as plates. Products with complex or subtle attributes needed a separately trained model or human review.
Technology and data
Amazon added large language models to this. First it groups the whole catalogue by product type and attribute value and builds statistics on how often and where each value appears. Among forms such as Bluetooth, BT and BT 5.1 for wireless headphones, it picks the candidate the statistics point to as best and tells the model. Fine distinctions that statistics alone cannot settle are handled with prompt tuning. The prompt carries an instruction to match the granularity of the candidate list so that more specific values, such as 440 stainless steel against stainless steel, are not erased. The model tuned this way does three jobs across the whole catalogue: recognising standard attribute values, collecting different forms that mean the same thing, and detecting wrong or nonsensical input.
Results
Amazon said that under the new approach the latest values submitted by sellers reach the catalogue within days. It explained that human review also saves thousands of hours. It said the number of languages it can monitor and update has grown. It said quality control now reaches corners of the catalogue that were too expensive to touch under the old approach.
Limits and open questions
The reference period and the calculation method behind the thousands of hours saved were not disclosed. This article has no figures on changes in purchases or revenue.
Sources
- Using LLMs to improve Amazon product listings - Amazon Scienceamazon.science, Accessed
Compiled from public sources. These are not results from ATF Works customers.