mlsanityData to 13 Oct 2026

Related Pins at Pinterest: The Evolution of a Real-World Recommender System

arXiv 1702.07969 ↗First posted 26 Feb 2017cs.IRcited by 24

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Abstract (arXiv)

Related Pins is the Web-scale recommender system that powers over 40% of user engagement on Pinterest. This paper is a longitudinal study of three years of its development, exploring the evolution of the system and its components from prototypes to present state. Each component was originally built with many constraints on engineering effort and computational resources, so we prioritized the simplest and highest-leverage solutions. We show how organic growth led to a complex system and how we managed this complexity. Many challenges arose while building this system, such as avoiding feedback loops, evaluating performance, activating content, and eliminating legacy heuristics. Finally, we offer suggestions for tackling these challenges when engineering Web-scale recommender systems.

Key facts. Cited by 24 papers in the map (data through 13 Oct 2026). First posted on arXiv 26 Feb 2017.

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Citing papers per year: 2021: 2; 2022: 9; 2023: 5; 2024: 2; 2025: 5; 2026: 1.

24 papers.

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  1. 2022Understanding Scaling Laws for Recommendation Modelscited by 38
  2. 2023TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterestcited by 35
  3. 2021Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representationscited by 24
  4. 2023High-Throughput Vector Similarity Search in Knowledge Graphscited by 19
  5. 2021Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameterscited by 17
  6. 2022AutoShard: Automated Embedding Table Sharding for Recommender Systemscited by 17
  7. 2022DreamShard: Generalizable Embedding Table Placement for Recommender Systemscited by 14
  8. 2022Recommendation Systems with Distribution-Free Reliability Guaranteescited by 13
  9. 2022BagPipe: Accelerating Deep Recommendation Model Trainingcited by 10
  10. 2023Online Matching: A Real-time Bandit System for Large-scale Recommendationscited by 8
  11. 2025Quake: Adaptive Indexing for Vector Searchcited by 8
  12. 2024Incremental IVF Index Maintenance for Streaming Vector Searchcited by 7
12 more
  1. 2025MicroNN: An On-device Disk-resident Updatable Vector Databasecited by 6
  2. 2023Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Modelscited by 5
  3. 2022kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrievalcited by 4
  4. 2022Learning to Rank For Push Notifications Using Pairwise Expected Regretcited by 3
  5. 2022Biomedical Multi-hop Question Answering Using Knowledge Graph Embeddings and Language Modelscited by 1
  6. 2023Impression-Aware Recommender Systemscited by 1
  7. 2024Position bias in featurescited by 1
  8. 2025Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systemscited by 1
  9. 2025Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterestcited by 1
  10. 2022MetaCon: Unified Predictive Segments System with Trillion Concept Meta-Learningcited by 0
  11. 2025Applying Deep Learning to Ads Conversion Prediction in Last Mile Delivery Marketplacecited by 0
  12. 2026Distributional Approximate Nearest Neighbour Search for Uncertainty-Aware Retrievalcited by 0