mlsanityData to 7 Oct 2026

An Embedding-Based Grocery Search Model at Instacart

arXiv 2209.05555 ↗First posted 12 Sep 2022cs.CL cs.IRcited by 5

Not on the map. Why?

Abstract (arXiv)

The key to e-commerce search is how to best utilize the large yet noisy log data. In this paper, we present our embedding-based model for grocery search at Instacart. The system learns query and product representations with a two-tower transformer-based encoder architecture. To tackle the cold-start problem, we focus on content-based features. To train the model efficiently on noisy data, we propose a self-adversarial learning method and a cascade training method. AccOn an offline human evaluation dataset, we achieve 10% relative improvement in RECALL@20, and for online A/B testing, we achieve 4.1% cart-adds per search (CAPS) and 1.5% gross merchandise value (GMV) improvement. We describe how we train and deploy the embedding based search model and give a detailed analysis of the effectiveness of our method.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 26 references that are in the map. First posted on arXiv 12 Sep 2022.

Builds on

20 of 26 references.

References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  7. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  8. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  9. 2017Billion-scale similarity search with GPUscited by 2,477
  10. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,414
  11. 2019TinyBERT: Distilling BERT for Natural Language Understandingcited by 1,153
  12. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
8 more
  1. 2016Wide & Deep Learning for Recommender Systemscited by 745
  2. 2020Embedding-based Retrieval in Facebook Searchcited by 155
  3. 2015Towards Universal Paraphrastic Sentence Embeddingscited by 59
  4. 2019Semantic Product Searchcited by 51
  5. 2021Embedding-based Product Retrieval in Taobao Searchcited by 50
  6. 2020Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learningcited by 31
  7. 2021Relation-aware Graph Attention Model With Adaptive Self-adversarial Trainingcited by 12
  8. 2020Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrievalcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 0; 2025: 0; 2026: 1.

5 papers.

  1. 2023Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplacecited by 9
  2. 2023Unified Embedding Based Personalized Retrieval in Etsy Searchcited by 3
  3. 2023Rethinking E-Commerce Searchcited by 2
  4. 2023Mitigating Pooling Bias in E-commerce Search via False Negative Estimationcited by 0
  5. 2026Mine and Refine: Optimizing Graded Relevance in E-commerce Semantic Search Retrievalcited by 0