# Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systems

arXiv: https://arxiv.org/abs/2511.18013 · HTML: https://mlsanity.com/paper/2511.18013/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://mlsanity.com/paper/2205.04507/) (2022)
- [Learning a Unified Embedding for Visual Search at Pinterest](https://mlsanity.com/paper/1908.01707/) (2019)
- [Related Pins at Pinterest: The Evolution of a Real-World Recommender System](https://mlsanity.com/paper/1702.07969/) (2017)
- [LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application](https://mlsanity.com/paper/2405.03988/) (2024)
- [User Retention-oriented Recommendation with Decision Transformer](https://mlsanity.com/paper/2303.06347/) (2023)
- [Modeling User Retention through Generative Flow Networks](https://mlsanity.com/paper/2406.06043/) (2024)
- [Intelligent Online Selling Point Extraction for E-Commerce Recommendation](https://mlsanity.com/paper/2112.10613/) (2021)
- [Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest](https://mlsanity.com/paper/1511.04003/) (2015)
- [Augmenting Sequential Recommendation with Balanced Relevance and Diversity](https://mlsanity.com/paper/2412.08300/) (2024)

## Built on by (most cited first, 2 of 2)

- [Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning](https://mlsanity.com/paper/2607.14192/) (2026)
- [FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems](https://mlsanity.com/paper/2610.13806/) (2026)

## Abstract (arXiv)

User retention is a critical objective for online platforms like Pinterest, as it strengthens user loyalty and drives growth through repeated engagement. A key indicator of retention is revisitation, i.e., when users return to view previously saved content, a behavior often sparked by personalized recommendations and user satisfaction. However, modeling and optimizing revisitation poses significant challenges. One core difficulty is accurate attribution: it is often unclear which specific user actions or content exposures trigger a revisit, since many confounding factors (e.g., content quality, user interface, notifications, or even changing user intent) can influence return behavior. Additionally, the scale and timing of revisitations introduce further complexity; users may revisit content days or even weeks after their initial interaction, requiring the system to maintain and associate extensive historical records across millions of users and sessions. These complexities render existing methods insufficient for robustly capturing and optimizing long-term revisitation. To address these gaps, we introduce a novel, lightweight, and interpretable framework for modeling revisitation behavior and optimizing long-term user retention in Pinterest's search-based recommendation context. By defining a surrogate attribution process that links saves to subsequent revisitations, we reduce noise in the causal relationship between user actions and return visits. Our scalable event aggregation pipeline enables large-scale analysis of user revisitation patterns and enhances the ranking system's ability to surface items with high retention value. Deployed on Pinterest's Related Pins surface to serve 500+ million users, the framework led to a significant lift of 0.1% in active users without additional computational costs.