Work

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

Year: 2024

Type: conference-paper

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 tradeoffs for multi-objective optimization. We evaluate our approach with live experiments and describe its deployment on YouTube.

Source: ACM Conference on Recommender Systems (RecSys)

DOI: 10.1145/3640457.3688184

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

Institution: Google (United States)

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4403220579

FWCI: 0.786

Cites: 13

Cited by: 2

Related to: 10