# PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest

arXiv: https://arxiv.org/abs/2607.22518 · HTML: https://mlsanity.com/paper/2607.22518/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 24 Jul 2026.

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

- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Recommender Systems with Generative Retrieval](https://mlsanity.com/paper/2305.05065/) (2023)
- [Neural Thompson Sampling](https://mlsanity.com/paper/2010.00827/) (2020)
- [Neural Contextual Bandits with Deep Representation and Shallow Exploration](https://mlsanity.com/paper/2012.01780/) (2020)
- [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://mlsanity.com/paper/2205.04507/) (2022)
- [Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations](https://mlsanity.com/paper/2306.08121/) (2023)
- [Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time](https://mlsanity.com/paper/1711.07601/) (2017)
- [TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest](https://mlsanity.com/paper/2306.00248/) (2023)
- [Learning a Unified Embedding for Visual Search at Pinterest](https://mlsanity.com/paper/1908.01707/) (2019)
- [ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest](https://mlsanity.com/paper/2205.11728/) (2022)
- [Long-Term Value of Exploration: Measurements, Findings and Algorithms](https://mlsanity.com/paper/2305.07764/) (2023)
- [Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation](https://mlsanity.com/paper/2306.01720/) (2023)
- [Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning](https://mlsanity.com/paper/2411.11225/) (2024)
- [Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest](https://mlsanity.com/paper/2512.17277/) (2025)

## Built on by (most cited first, 1 of 1)

- [Content Exploration Beyond the Feed: Creator Supply and the Shared Corpus](https://mlsanity.com/paper/2608.29430/) (2026)

## Abstract (arXiv)

In this paper, we propose a new solution for addressing the content cold-start problem in industry-scale search and recommender systems. Compared to prior approaches, we have made the following new contributions: 1) our solution spans the entire multi-stage funnel and generalizes well for both search and recommendation surfaces, 2) our solution reduces bias favoring existing content, allowing more accurate model prediction across content types and reducing short-term tradeoffs associated with high volumes of explicit content exploration, 3) our solution is evaluated with a scalable measurement framework that enables fast short-term experimentation while validating long-term impact. We have iteratively built and successfully deployed this new system at Pinterest in the past two years and observed significant improvements in fresh content exploration, overall user engagement, and content ecosystem health.