Towards Translating Objective Product Attributes into Customer Language (Amazon Science)
Source
- Title: Towards translating objective product attributes into customer language
- Publisher: Amazon Science
- Link: https://www.amazon.science/publications/towards-translating-objective-product-attributes-into-customer
- Type: Industry publication / research page
- The work targets the vocabulary mismatch problem in e-commerce: product catalogs store objective attributes (specifications, dimensions, materials), while customers search and review products using subjective, experiential language.
- Translating attributes into customer language can improve query-product relevance, search recall, and recommendation understanding, especially for long-tail queries where literal attribute matching fails.
- The item is indexed in a curated search/recommendation reading list under the Information Retrieval vertical, alongside related entries on query-product relevance labeling with LLMs and behavior-driven query similarity.
- Automated Query-Product Relevance Labeling using Large Language Models
- Behavior-driven query similarity prediction based on pre-trained language models
- Behavior Modeling Space Reconstruction for E-Commerce Search
- Better to Ask in English: Cross-Lingual Evaluation of Large Language Models
Key points
Context in modern search and recommendation systems
Attribute-to-customer-language translation sits at the intersection of:
1. Semantic matching — aligning structured catalog fields with free-form user queries. 2. Cold-start handling — generating customer-facing descriptions for new products lacking behavioral data. 3. LLM-era interaction — enabling natural-language and conversational shopping experiences where rigid attribute values are not directly consumable.
Caveat
This forum post is primarily a metadata digest. It reproduces the original title and source link but does not include the paper's full abstract, author list, experimental results, or numerical benchmarks. Readers should consult the Amazon Science page directly for authoritative details before citing any quantitative claims.