# Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction

arXiv: https://arxiv.org/abs/2408.06512 · HTML: https://mlsanity.com/paper/2408.06512/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems](https://mlsanity.com/paper/2208.04560/) (2022)
- [Reinforcing User Retention in a Billion Scale Short Video Recommender System](https://mlsanity.com/paper/2302.01724/) (2023)
- [Reward Shaping for User Satisfaction in a REINFORCE Recommender](https://mlsanity.com/paper/2209.15166/) (2022)

## Built on by (most cited first, 4 of 4)

- [Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents](https://mlsanity.com/paper/2602.10226/) (2026)
- [ACT: Automated Constraint Targeting for Multi-Objective Recommender Systems](https://mlsanity.com/paper/2509.03661/) (2025)
- [Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems](https://mlsanity.com/paper/2609.25433/) (2026)
- [Learned Cross-Task Relationships in Multi-Task Models](https://mlsanity.com/paper/2609.28776/) (2026)

## Abstract (arXiv)

We present the Learned Ranking Function (LRF), a system that takes short-term user-item behavior predictions as input and outputs a slate of recommendations that directly optimizes for long-term user satisfaction. Most previous work is based on optimizing the hyperparameters of a heuristic function. We propose to model the problem directly as a slate optimization problem with the objective of maximizing long-term user satisfaction. We also develop a novel constraint optimization algorithm that stabilizes objective trade-offs for multi-objective optimization. We evaluate our approach with live experiments and describe its deployment on YouTube.