Abstract
To surface products that are relevant to customers, search engines require online merchants to fill in certain attributes relating to their products. However, merchants often do not fill in the requested attributes, which can lead to search results not surfacing the most appropriate products and merchants. This disclosure describes techniques that leverage a large language model (LLM) to automatically determine and assign attributes to products in the inventory of a merchant. Per the techniques, available product information such as product title, category, image, brief description, etc. is used to predict with high probability the values of unfilled attributes. Attributes thus filled with the help of the LLM are applied to source data in a search engine or other system that supports product search. By leveraging an LLM, optionally trained on a large catalog and/or fine-tuned based on feedback from human raters to complete the attribute set of a product, the requirement of manual entry of product attributes is obviated and products can be characterized at scale. Upon automatically filling in the predicted attribute values, a higher likelihood of merchant products being surfaced to suitable customers is realized.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Pillai, Gokul; Taqdees, Sandaleen; Agrawal, Rishabh; Stillwell, Alex; Chinoski, Joey; and Meruva, Sree, "Automatically Determining Missing Product Attributes Using a Large Language Model", Technical Disclosure Commons, (November 28, 2024)
https://www.tdcommons.org/dpubs_series/7605