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

arXiv: https://arxiv.org/abs/2209.08435 · HTML: https://mlsanity.com/paper/2209.08435/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 18 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Learning Deep Transformer Models for Machine Translation](https://mlsanity.com/paper/1906.01787/) (2019)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Transformers without Tears: Improving the Normalization of Self-Attention](https://mlsanity.com/paper/1910.05895/) (2019)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://mlsanity.com/paper/1904.08030/) (2019)
- [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://mlsanity.com/paper/2205.04507/) (2022)
- [Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time](https://mlsanity.com/paper/1711.07601/) (2017)
- [Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning](https://mlsanity.com/paper/2006.02282/) (2020)
- [Visual Search at Pinterest](https://mlsanity.com/paper/1505.07647/) (2015)
- [Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images](https://mlsanity.com/paper/1611.08321/) (2016)

## Built on by (most cited first, 16 of 16)

- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [PersonalLLM: Tailoring LLMs to Individual Preferences](https://mlsanity.com/paper/2409.20296/) (2024)
- [Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models](https://mlsanity.com/paper/2310.20081/) (2023)
- [TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest](https://mlsanity.com/paper/2306.00248/) (2023)
- [Large Language Model Augmented Narrative Driven Recommendations](https://mlsanity.com/paper/2306.02250/) (2023)
- [Learning Personalized Alignment for Evaluating Open-ended Text Generation](https://mlsanity.com/paper/2310.03304/) (2023)
- [TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation](https://mlsanity.com/paper/2506.02267/) (2025)
- [PinRec: Unified Generative Retrieval for Pinterest Recommender Systems](https://mlsanity.com/paper/2504.10507/) (2025)
- [Request-Only Optimization for Recommendation Systems](https://mlsanity.com/paper/2508.05640/) (2025)
- [adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads](https://mlsanity.com/paper/2302.01255/) (2023)
- [On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction](https://mlsanity.com/paper/2504.08169/) (2025)
- [RecoMind: A Reinforcement Learning Framework for Optimizing In-Session User Satisfaction in Recommendation Systems](https://mlsanity.com/paper/2508.00201/) (2025)
- [Learning to Personalize Recommendation based on Customers' Shopping Intents](https://mlsanity.com/paper/2305.05279/) (2023)
- [Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest](https://mlsanity.com/paper/2509.05292/) (2025)
- [BiasEdit: A Training-Free Bias-Detect-and-Edit Framework for Learning Fair Visual Classifiers](https://mlsanity.com/paper/2605.28450/) (2026)
- [CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models](https://mlsanity.com/paper/2506.17281/) (2025)

## 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.