用二进制向量匹配字符型编码，提升电商搜索建议准确率
Improving Search Suggestions for Alphanumeric Queries
- 将字母数字串转为固定长度二进制向量，支持快速相似度计算
- 在真实电商数据上，搜索建议点击率提升显著（未提具体数值）
- 无需训练、可解释性强，适合线上系统快速部署
制造商零件号（MPNs）、SKU 和型号代码等字母数字标识在电商目录和搜索中广泛存在。这些标识稀疏、非语言化，且对分词和拼写变化极为敏感，导致传统基于词汇或嵌入的检索方法失效。我们提出一种无训练、基于字符级别的检索框架，将每个字母数字序列编码为固定长度的二进制向量。该表示支持通过汉明距离高效计算相似性，并可在大规模标识语料库上实现最近邻检索。可选的编辑距离重排序阶段进一步提升精度，同时保持低延迟。该方法为学习型密集检索模型提供了实用且可解释的替代方案，适用于搜索建议生成系统的生产部署。A/B 测试中业务指标显著提升，验证了其有效性。
原文摘要 · Abstract (English)
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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