Embedding-based Product Retrieval in Taobao Search
Sen Li, Fuyu Lv, Taiwei Jin, Guli Lin, Keping Yang, Xiaoyi Zeng,, Xiao-Ming Wu, Qianli Ma

TL;DR
This paper introduces MGDSPR, a novel embedding-based product retrieval model for Taobao Search that improves relevance and convergence, addressing issues of relevance and training-inference discrepancy in existing systems.
Contribution
The paper proposes MGDSPR, a new retrieval model that enhances relevance and training efficiency, with practical deployment insights for large-scale e-commerce search systems.
Findings
Significant offline metric improvements in product relevance.
Successful online A/B test results demonstrating system effectiveness.
Effective handling of noisy data and hard negatives without extra resources.
Abstract
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…
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Taxonomy
TopicsText and Document Classification Technologies · Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques
Methodstravel james · Softmax
