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PinEqualizer addresses the content cold-start problem in large-scale search and recommender systems by spanning the entire multi-stage funnel to reduce bias toward existing items. This approach improves model prediction accuracy across diverse content types while mitigating short-term tradeoffs associated with high volumes of fresh exploration, ultimately enhancing user engagement and ecosystem health at Pinterest.
PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest
arXiv:2607.22518v1 Announce Type: cross Abstract: 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.
Abstract: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.
10 pages, 2 figures. Accepted at KDD 2026
Information Retrieval (cs.IR); Machine Learning (cs.LG)
arXiv:2607.22518 [cs.IR]
(or arXiv:2607.22518v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2607.22518 arXiv-issued DOI via DataCite (pending registration)
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