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

• URL: http://arxiv.org/abs/2408.06512v1
• Date: Mon, 12 Aug 2024 22:02:39 GMT
• Title: Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction
• Authors: Yi Wu, Daryl Chang, Jennifer She, Zhe Zhao, Li Wei, Lukasz Heldt,
• Abstract summary: 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. We propose to model the problem directly as a slate optimization problem with the objective of maximizing long-term user satisfaction.
• Score: 11.109665449393738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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