Improving Search Suggestions for Alphanumeric Queries

TL;DR

This paper introduces a character-level, training-free retrieval method for alphanumeric search queries that improves efficiency and accuracy in e-commerce search suggestions, outperforming traditional models.

Contribution

The authors present a novel fixed-length binary vector encoding for alphanumeric identifiers, enabling fast similarity search with optional re-ranking, suitable for production environments.

Findings

Significant business metric improvements in A/B testing

Efficient retrieval via Hamming distance over large corpora

Enhanced precision with optional edit distance re-ranking

Abstract

Alphanumeric identifiers such as manufacturer part numbers (MPNs), SKUs, and model codes are ubiquitous in e-commerce catalogs and search. These identifiers are sparse, non linguistic, and highly sensitive to tokenization and typographical variation, rendering conventional lexical and embedding based retrieval methods ineffective. We propose a training free, character level retrieval framework that encodes each alphanumeric sequence as a fixed length binary vector. This representation enables efficient similarity computation via Hamming distance and supports nearest neighbor retrieval over large identifier corpora. An optional re-ranking stage using edit distance refines precision while preserving latency guarantees. The method offers a practical and interpretable alternative to learned dense retrieval models, making it suitable for production deployment in search suggestion generation systems. Significant gains in business metrics in the A/B test further prove utility of our approach.