mlsanityData to 7 Oct 2026

Embedding-based Retrieval in Facebook Search

arXiv 2006.11632 ↗First posted 20 Jun 2020cs.IRcited by 155

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Abstract (arXiv)

Search in social networks such as Facebook poses different challenges than in classical web search: besides the query text, it is important to take into account the searcher's context to provide relevant results. Their social graph is an integral part of this context and is a unique aspect of Facebook search. While embedding-based retrieval (EBR) has been applied in eb search engines for years, Facebook search was still mainly based on a Boolean matching model. In this paper, we discuss the techniques for applying EBR to a Facebook Search system. We introduce the unified embedding framework developed to model semantic embeddings for personalized search, and the system to serve embedding-based retrieval in a typical search system based on an inverted index. We discuss various tricks and experiences on end-to-end optimization of the whole system, including ANN parameter tuning and full-stack optimization. Finally, we present our progress on two selected advanced topics about modeling. We evaluated EBR on verticals for Facebook Search with significant metrics gains observed in online A/B experiments. We believe this paper will provide useful insights and experiences to help people on developing embedding-based retrieval systems in search engines.

Key facts. Cited by 155 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2020.

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Citing papers per year: 2021: 16; 2022: 29; 2023: 28; 2024: 24; 2025: 38; 2026: 20.

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  1. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  2. 2022MTEB: Massive Text Embedding Benchmarkcited by 623
  3. 2024The Faiss librarycited by 370
  4. 2024Generative Representational Instruction Tuningcited by 227
  5. 2021RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-rankingcited by 175
  6. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  7. 2021GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrievalcited by 119
  8. 2021Adversarial Retriever-Ranker for dense text retrievalcited by 82
  9. 2021TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Searchcited by 54
  10. 2021Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmarkcited by 47
  11. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  12. 2025MemVerse: Multimodal Memory for Lifelong Learning Agentscited by 31
38 more
  1. 2025Search Arena: Analyzing Search-Augmented LLMscited by 30
  2. 2022Fine-Grained Distillation for Long Document Retrievalcited by 29
  3. 2021A Proposed Conceptual Framework for a Representational Approach to Information Retrievalcited by 24
  4. 2022ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterestcited by 23
  5. 2024Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insightscited by 23
  6. 2022Multi-view Multi-behavior Contrastive Learning in Recommendationcited by 21
  7. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  8. 2022HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Rerankingcited by 19
  9. 2023Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendationcited by 19
  10. 2023Embedding in Recommender Systems: A Surveycited by 19
  11. 2024PL-MTEB: Polish Massive Text Embedding Benchmarkcited by 19
  12. 2022Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Searchcited by 17
  13. 2022Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddingscited by 17
  14. 2022LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrievalcited by 15
  15. 2022DeepJoin: Joinable Table Discovery with Pre-trained Language Modelscited by 15
  16. 2023Tsetlin Machine Embedding: Representing Words Using Logical Expressionscited by 15
  17. 2022Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendationcited by 14
  18. 2023Revisiting Neural Retrieval on Acceleratorscited by 14
  19. 2024Does Negative Sampling Matter? A Review with Insights into its Theory and Applicationscited by 14
  20. 2022Heterogeneous Acceleration Pipeline for Recommendation System Trainingcited by 13
  21. 2023Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendationcited by 13
  22. 2024German Text Embedding Clustering Benchmarkcited by 13
  23. 2024OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Searchcited by 13
  24. 2024Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimizationcited by 11
  25. 2025MIEB: Massive Image Embedding Benchmarkcited by 11
  26. 2023Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Modelscited by 10
  27. 2024VectorSearch: Enhancing Document Retrieval with Semantic Embeddings and Optimized Searchcited by 10
  28. 2021Graph-based Multilingual Product Retrieval in E-commerce Searchcited by 9
  29. 2021Rethinking Self-Supervision Objectives for Generalizable Coherence Modelingcited by 8
  30. 2022Supervised Contrastive Learning for Recommendationcited by 7
  31. 2022Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrievalcited by 7
  32. 2022Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendationcited by 7
  33. 2023BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphscited by 7
  34. 2023Cold & Warm Net: Addressing Cold-Start Users in Recommender Systemscited by 7
  35. 2025FreeRet: MLLMs as Training-Free Retrieverscited by 7
  36. 2022Modality-Balanced Embedding for Video Retrievalcited by 6
  37. 2022Multi-Objective Personalized Product Retrieval in Taobao Searchcited by 6
  38. 2022SeDR: Segment Representation Learning for Long Documents Dense Retrievalcited by 6

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