Can I read Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction on EtoBox?

Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction by Wu, Yi; Chang, Daryl; She, Jennifer; Zhao, Zhe; Wei, Li; Heldt, Lukasz is a scholarly article available to read on EtoBox.

What is Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction about?

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.

Author

Wu, Yi; Chang, Daryl; She, Jennifer; Zhao, Zhe; Wei, Li; Heldt, Lukasz

Published

2024

Language

EN