Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction
<|reference_start|>Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction: 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.<|reference_end|>
@article{wu2024learned,
    title={Learned Ranking Function: From Short-term Behavior Predictions to
  Long-term User Satisfaction},
    author={Yi Wu, Daryl Chang, Jennifer She, Zhe Zhao, Li Wei, Lukasz Heldt},
    journal={arXiv preprint arXiv:2408.06512},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06512},
    primaryClass={cs.LG cs.AI cs.IR}
}