# Embedding-based Product Retrieval in Taobao Search

arXiv: https://arxiv.org/abs/2106.09297 · HTML: https://mlsanity.com/paper/2106.09297/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2021.

## Built on by (most cited first, 30 of 51)

- [Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach](https://mlsanity.com/paper/2305.07001/) (2023)
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- [Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings](https://mlsanity.com/paper/2204.00185/) (2022)
- [OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search](https://mlsanity.com/paper/2404.16260/) (2024)
- [Intelligent Request Strategy Design in Recommender System](https://mlsanity.com/paper/2206.12296/) (2022)
- [MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search](https://mlsanity.com/paper/2301.12646/) (2023)
- [Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplace](https://mlsanity.com/paper/2302.11052/) (2023)
- [Large Language Models for Relevance Judgment in Product Search](https://mlsanity.com/paper/2406.00247/) (2024)
- [LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval](https://mlsanity.com/paper/2601.14706/) (2026)
- [Modality-Balanced Embedding for Video Retrieval](https://mlsanity.com/paper/2204.08182/) (2022)
- [Multi-Objective Personalized Product Retrieval in Taobao Search](https://mlsanity.com/paper/2210.04170/) (2022)
- [Binary Embedding-based Retrieval at Tencent](https://mlsanity.com/paper/2302.08714/) (2023)
- [An Embedding-Based Grocery Search Model at Instacart](https://mlsanity.com/paper/2209.05555/) (2022)
- [Database-Augmented Query Representation for Information Retrieval](https://mlsanity.com/paper/2406.16013/) (2024)
- [MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understanding](https://mlsanity.com/paper/2511.12449/) (2025)
- [A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data](https://mlsanity.com/paper/2211.02533/) (2022)
- [Machine Translation Impact in E-commerce Multilingual Search](https://mlsanity.com/paper/2302.00119/) (2023)
- [Unified Embedding Based Personalized Retrieval in Etsy Search](https://mlsanity.com/paper/2306.04833/) (2023)
- [REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing](https://mlsanity.com/paper/2506.16444/) (2025)
- [Embedding-based Retrieval in Multimodal Content Moderation](https://mlsanity.com/paper/2507.01066/) (2025)
- [Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search](https://mlsanity.com/paper/2304.02089/) (2023)
- [Towards Generalizable Semantic Product Search by Text Similarity Pre-training on Search Click Logs](https://mlsanity.com/paper/2204.05231/) (2022)
- [Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Search](https://mlsanity.com/paper/2304.09287/) (2023)
- [Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs](https://mlsanity.com/paper/2305.16344/) (2023)
- [Rethinking E-Commerce Search](https://mlsanity.com/paper/2312.03217/) (2023)
- [Multimodal semantic retrieval for product search](https://mlsanity.com/paper/2501.07365/) (2025)
- [Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation](https://mlsanity.com/paper/2505.16065/) (2025)
- [LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval](https://mlsanity.com/paper/2302.02908/) (2023)
- [Learning to Retrieve for Job Matching](https://mlsanity.com/paper/2402.13435/) (2024)

## Abstract (arXiv)

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.