# Semantic Product Search

arXiv: https://arxiv.org/abs/1907.00937 · HTML: https://mlsanity.com/paper/1907.00937/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

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

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Extreme Multi-label Learning for Semantic Matching in Product Search](https://mlsanity.com/paper/2106.12657/) (2021)
- [ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest](https://mlsanity.com/paper/2205.11728/) (2022)
- [OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search](https://mlsanity.com/paper/2404.16260/) (2024)
- [RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2408.08933/) (2024)
- [Large-Scale Training System for 100-Million Classification at Alibaba](https://mlsanity.com/paper/2102.06025/) (2021)
- [A survey on fairness of large language models in e-commerce: progress, application, and challenge](https://mlsanity.com/paper/2405.13025/) (2024)
- [Graph-based Multilingual Product Retrieval in E-commerce Search](https://mlsanity.com/paper/2105.02978/) (2021)
- [MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search](https://mlsanity.com/paper/2301.12646/) (2023)
- [Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation](https://mlsanity.com/paper/2208.11457/) (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 Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce](https://mlsanity.com/paper/2403.00923/) (2024)
- [Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings](https://mlsanity.com/paper/2403.14001/) (2024)
- [Preference Discerning with LLM-Enhanced Generative Retrieval](https://mlsanity.com/paper/2412.08604/) (2024)
- [An Embedding-Based Grocery Search Model at Instacart](https://mlsanity.com/paper/2209.05555/) (2022)
- [Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding](https://mlsanity.com/paper/2210.03915/) (2022)
- [Reasonable Scale Machine Learning with Open-Source Metaflow](https://mlsanity.com/paper/2303.11761/) (2023)
- [Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations](https://mlsanity.com/paper/2104.02667/) (2021)
- [Machine Translation Impact in E-commerce Multilingual Search](https://mlsanity.com/paper/2302.00119/) (2023)
- [adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads](https://mlsanity.com/paper/2302.01255/) (2023)
- [Unified Embedding Based Personalized Retrieval in Etsy Search](https://mlsanity.com/paper/2306.04833/) (2023)
- [PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models](https://mlsanity.com/paper/2312.02429/) (2023)
- [Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce](https://mlsanity.com/paper/2407.09395/) (2024)
- [Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models](https://mlsanity.com/paper/2505.07105/) (2025)
- [Semantic Retrieval at Walmart](https://mlsanity.com/paper/2412.04637/) (2024)
- [Embracing Structure in Data for Billion-Scale Semantic Product Search](https://mlsanity.com/paper/2110.06125/) (2021)
- [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)
- [SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search](https://mlsanity.com/paper/2308.07711/) (2023)

## Abstract (arXiv)

We study the problem of semantic matching in product search, that is, given a customer query, retrieve all semantically related products from the catalog. Pure lexical matching via an inverted index falls short in this respect due to several factors: a) lack of understanding of hypernyms, synonyms, and antonyms, b) fragility to morphological variants (e.g. "woman" vs. "women"), and c) sensitivity to spelling errors. To address these issues, we train a deep learning model for semantic matching using customer behavior data. Much of the recent work on large-scale semantic search using deep learning focuses on ranking for web search. In contrast, semantic matching for product search presents several novel challenges, which we elucidate in this paper. We address these challenges by a) developing a new loss function that has an inbuilt threshold to differentiate between random negative examples, impressed but not purchased examples, and positive examples (purchased items), b) using average pooling in conjunction with n-grams to capture short-range linguistic patterns, c) using hashing to handle out of vocabulary tokens, and d) using a model parallel training architecture to scale across 8 GPUs. We present compelling offline results that demonstrate at least 4.7% improvement in Recall@100 and 14.5% improvement in mean average precision (MAP) over baseline state-of-the-art semantic search methods using the same tokenization method. Moreover, we present results and discuss learnings from online A/B tests which demonstrate the efficacy of our method.