String matching algorithms in the presence of abbreviations, such as in Stock Keeping Unit (SKU) product catalogs, remains a relatively unexplored topic. In this paper, we present a unified architecture for SKU search that provides both a real-time suggestion system (based on a Trie data structure) as well as a lower latency search system (making use of character level TF-IDF in combination with language model vector embeddings) where users initiate the search process explicitly. We carry out ablation studies that justify designing a complex search system composed of multiple components to address the delicate trade-off between speed and accuracy. Using SKU search in the Dynamics CRM as an example, we show how our system vastly outperforms, in all aspects, the results provided by the default search engine. Finally, we show how SKU descriptions may be enhanced via generative text models (using gpt-3.5-turbo) so that the consumers of the search results may get more context and a generally better experience when presented with the results of their SKU search.

在存在缩略词的情况下，本论文提出了一种适用于库存统一编码产品目录的字符串匹配算法。我们提供了统一的架构用于 SKU 搜索，其中包括实时建议系统（基于 Trie 数据结构）和低延迟搜索系统（使用字符级 TF-IDF 结合语言模型向量嵌入）。我们通过割除研究证明了构建由多个组件组成的复杂搜索系统是合理的，以解决速度和准确性之间微妙的权衡。以 Dynamics CRM 中的 SKU 搜索为例，我们展示了我们的系统在所有方面都远远超过了默认搜索引擎提供的结果。最后，我们展示了如何通过生成式文本模型（使用 gpt-3.5-turbo）改善 SKU 描述，以便搜索结果的使用者可以获得更多上下文和更好的体验。

搜索、快速且慢速地浏览产品目录