# Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning

arXiv: https://arxiv.org/abs/2006.02282 · HTML: https://mlsanity.com/paper/2006.02282/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 30 of 32)

- [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://mlsanity.com/paper/2205.04507/) (2022)
- [DeepShop: A Benchmark for Deep Research Shopping Agents](https://mlsanity.com/paper/2506.02839/) (2025)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest](https://mlsanity.com/paper/2205.11728/) (2022)
- [Rethinking Personalized Ranking at Pinterest: An End-to-End Approach](https://mlsanity.com/paper/2209.08435/) (2022)
- [Query Rewriting via Cycle-Consistent Translation for E-Commerce Search](https://mlsanity.com/paper/2103.00800/) (2021)
- [e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce](https://mlsanity.com/paper/2207.00208/) (2022)
- [Generative Retrieval with Preference Optimization for E-commerce Search](https://mlsanity.com/paper/2407.19829/) (2024)
- [Large Language Models for Relevance Judgment in Product Search](https://mlsanity.com/paper/2406.00247/) (2024)
- [Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation](https://mlsanity.com/paper/2208.11457/) (2022)
- [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)
- [A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data](https://mlsanity.com/paper/2211.02533/) (2022)
- [Unified Embedding Based Personalized Retrieval in Etsy Search](https://mlsanity.com/paper/2306.04833/) (2023)
- [FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications](https://mlsanity.com/paper/2511.14865/) (2025)
- [Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification](https://mlsanity.com/paper/2308.09308/) (2023)
- [Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes](https://mlsanity.com/paper/2203.05082/) (2022)
- [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)
- [Rethinking E-Commerce Search](https://mlsanity.com/paper/2312.03217/) (2023)
- [MRSE: An Efficient Multi-modality Retrieval System for Large Scale E-commerce](https://mlsanity.com/paper/2408.14968/) (2024)
- [Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?](https://mlsanity.com/paper/2304.04330/) (2023)
- [Compressing Search with Language Models](https://mlsanity.com/paper/2407.00085/) (2024)
- [pEBR: A Probabilistic Approach to Embedding Based Retrieval](https://mlsanity.com/paper/2410.19349/) (2024)
- [Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap](https://mlsanity.com/paper/2501.14826/) (2025)
- [Bootstrapping Conditional Retrieval for User-to-Item Recommendations](https://mlsanity.com/paper/2508.16793/) (2025)
- [DSIRM: Learning Query-Bridged Discrete Semantic Identifiers for E-commerce Relevance Modeling](https://mlsanity.com/paper/2606.04374/) (2026)

## Abstract (arXiv)

Nowadays e-commerce search has become an integral part of many people's shopping routines. Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query terms, and how to retrieve items that are more personalized to different users for the same search query. In this paper, we present a novel approach called DPSR, which stands for Deep Personalized and Semantic Retrieval, to tackle this problem. Explicitly, we share our design decisions on how to architect a retrieval system so as to serve industry-scale traffic efficiently and how to train a model so as to learn query and item semantics accurately. Based on offline evaluations and online A/B test with live traffics, we show that DPSR model outperforms existing models, and DPSR system can retrieve more personalized and semantically relevant items to significantly improve users' search experience by +1.29% conversion rate, especially for long tail queries by +10.03%. As a result, our DPSR system has been successfully deployed into JD.com's search production since 2019.