Nowadays, the product search service of e-commerce platforms has become a
vital shopping channel in people's life. The retrieval phase of products
determines the search system's quality and gradually attracts researchers'
attention. Retrieving the most relevant products from a large-scale corpus
while preserving personalized user characteristics remains an open question.
Recent approaches in this domain have mainly focused on embedding-based
retrieval (EBR) systems. However, after a long period of practice on Taobao, we
find that the performance of the EBR system is dramatically degraded due to
its: (1) low relevance with a given query and (2) discrepancy between the
training and inference phases. Therefore, we propose a novel and practical
embedding-based product retrieval model, named Multi-Grained Deep Semantic
Product Retrieval (MGDSPR). Specifically, we first identify the inconsistency
between the training and inference stages, and then use the softmax
cross-entropy loss as the training objective, which achieves better performance
and faster convergence. Two efficient methods are further proposed to improve
retrieval relevance, including smoothing noisy training data and generating
relevance-improving hard negative samples without requiring extra knowledge and
training procedures. We evaluate MGDSPR on Taobao Product Search with
significant metrics gains observed in offline experiments and online A/B tests.
MGDSPR has been successfully deployed to the existing multi-channel retrieval
system in Taobao Search. We also introduce the online deployment scheme and
share practical lessons of our retrieval system to contribute to the community.

本文探讨了电子商务平台中产品检索服务的质量问题。针对嵌入式检索系统中存在的问题，提出了一种新的多粒度深层语义产品检索模型。改进的算法通过平滑噪声数据和生成硬负样本来提高相关性，并在淘宝搜索上进行了线下和在线实验，证明了其有效性，并成功应用于现有的多通道检索系统中。