mlsanityData to 7 Oct 2026

Rethinking Personalized Ranking at Pinterest: An End-to-End Approach

arXiv 2209.08435 ↗First posted 18 Sep 2022cs.IR cs.AI cs.LGcited by 16

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Abstract (arXiv)

In this work, we present our journey to revolutionize the personalized recommendation engine through end-to-end learning from raw user actions. We encode user's long-term interest in Pinner- Former, a user embedding optimized for long-term future actions via a new dense all-action loss, and capture user's short-term intention by directly learning from the real-time action sequences. We conducted both offline and online experiments to validate the performance of the new model architecture, and also address the challenge of serving such a complex model using mixed CPU/GPU setup in production. The proposed system has been deployed in production at Pinterest and has delivered significant online gains across organic and Ads applications.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 18 Sep 2022.

Builds on

15 of 20 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  4. 2018Deep Interest Evolution Network for Click-Through Rate Predictioncited by 336
  5. 2019Learning Deep Transformer Models for Machine Translationcited by 309
  6. 2020Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Predictioncited by 176
  7. 2019Transformers without Tears: Improving the Normalization of Self-Attentioncited by 153
  8. 2019Behavior Sequence Transformer for E-commerce Recommendation in Alibabacited by 128
  9. 2019Practice on Long Sequential User Behavior Modeling for Click-Through Rate Predictioncited by 118
  10. 2019Multi-Interest Network with Dynamic Routing for Recommendation at Tmallcited by 113
  11. 2022PinnerFormer: Sequence Modeling for User Representation at Pinterestcited by 57
  12. 2017Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Timecited by 46
3 more
  1. 2020Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learningcited by 31
  2. 2015Visual Search at Pinterestcited by 19
  3. 2016Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Imagescited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 1; 2025: 7; 2026: 1.

16 papers.

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  1. 2023LaMP: When Large Language Models Meet Personalizationcited by 425
  2. 2024PersonalLLM: Tailoring LLMs to Individual Preferencescited by 56
  3. 2023Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Modelscited by 40
  4. 2023TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterestcited by 35
  5. 2023Large Language Model Augmented Narrative Driven Recommendationscited by 31
  6. 2023Learning Personalized Alignment for Evaluating Open-ended Text Generationcited by 29
  7. 2025PinRec: Unified Generative Retrieval for Pinterest Recommender Systemscited by 8
  8. 2025TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendationcited by 8
  9. 2025Request-Only Optimization for Recommendation Systemscited by 5
  10. 2023adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Adscited by 4
  11. 2025On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Predictioncited by 3
  12. 2025RecoMind: A Reinforcement Learning Framework for Optimizing In-Session User Satisfaction in Recommendation Systemscited by 3
4 more
  1. 2023Learning to Personalize Recommendation based on Customers' Shopping Intentscited by 1
  2. 2025Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterestcited by 1
  3. 2026BiasEdit: A Training-Free Bias-Detect-and-Edit Framework for Learning Fair Visual Classifierscited by 1
  4. 2025CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Modelscited by 0