Improving Search Suggestions for Alphanumeric Queries

• URL: http://arxiv.org/abs/2604.07364v1
• Date: Wed, 01 Apr 2026 19:38:36 GMT
• Title: Improving Search Suggestions for Alphanumeric Queries
• Abstract summary: We propose a training free, character level retrieval framework that encodes each alphanumeric sequence as a fixed length binary vector.<n>This representation enables efficient similarity via Hamming distance and supports nearest neighbor retrieval over large identifier corpora.
• Score: 13.198808921085943
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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