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

arXiv: https://arxiv.org/abs/1702.07969 · HTML: https://mlsanity.com/paper/1702.07969/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2017.

## Built on by (most cited first, 24 of 24)

- [Understanding Scaling Laws for Recommendation Models](https://mlsanity.com/paper/2208.08489/) (2022)
- [TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest](https://mlsanity.com/paper/2306.00248/) (2023)
- [Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations](https://mlsanity.com/paper/2108.05887/) (2021)
- [High-Throughput Vector Similarity Search in Knowledge Graphs](https://mlsanity.com/paper/2304.01926/) (2023)
- [Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters](https://mlsanity.com/paper/2111.05897/) (2021)
- [AutoShard: Automated Embedding Table Sharding for Recommender Systems](https://mlsanity.com/paper/2208.06399/) (2022)
- [DreamShard: Generalizable Embedding Table Placement for Recommender Systems](https://mlsanity.com/paper/2210.02023/) (2022)
- [Recommendation Systems with Distribution-Free Reliability Guarantees](https://mlsanity.com/paper/2207.01609/) (2022)
- [BagPipe: Accelerating Deep Recommendation Model Training](https://mlsanity.com/paper/2202.12429/) (2022)
- [Online Matching: A Real-time Bandit System for Large-scale Recommendations](https://mlsanity.com/paper/2307.15893/) (2023)
- [Quake: Adaptive Indexing for Vector Search](https://mlsanity.com/paper/2506.03437/) (2025)
- [Incremental IVF Index Maintenance for Streaming Vector Search](https://mlsanity.com/paper/2411.00970/) (2024)
- [MicroNN: An On-device Disk-resident Updatable Vector Database](https://mlsanity.com/paper/2504.05573/) (2025)
- [Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://mlsanity.com/paper/2305.01868/) (2023)
- [kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval](https://mlsanity.com/paper/2205.06205/) (2022)
- [Learning to Rank For Push Notifications Using Pairwise Expected Regret](https://mlsanity.com/paper/2201.07681/) (2022)
- [Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systems](https://mlsanity.com/paper/2511.18013/) (2025)
- [Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest](https://mlsanity.com/paper/2512.17277/) (2025)
- [Biomedical Multi-hop Question Answering Using Knowledge Graph Embeddings and Language Models](https://mlsanity.com/paper/2211.05351/) (2022)
- [Impression-Aware Recommender Systems](https://mlsanity.com/paper/2308.07857/) (2023)
- [Position bias in features](https://mlsanity.com/paper/2402.02626/) (2024)
- [MetaCon: Unified Predictive Segments System with Trillion Concept Meta-Learning](https://mlsanity.com/paper/2203.04540/) (2022)
- [Applying Deep Learning to Ads Conversion Prediction in Last Mile Delivery Marketplace](https://mlsanity.com/paper/2502.10514/) (2025)
- [Distributional Approximate Nearest Neighbour Search for Uncertainty-Aware Retrieval](https://mlsanity.com/paper/2606.04603/) (2026)

## 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.