RANKING AND RECOMMENDATIONS

REINFORCEMENT LEARNING

Reinforcement Learning-based Product Delivery Frequency Control

December 17, 2020

Abstract

Frequency control is an important problem in modern recommender systems. It dictates the delivery frequency of recommendations to maintain product quality and efficiency. For example, the frequency of delivering promotional notifications impacts daily metrics as well as the infrastructure resource consumption (e.g. CPU and memory usage). There remain open questions on what objective we should optimize to represent business values in the long term best, and how we should balance between daily metrics and resource consumption in a dynamically fluctuating environment. We propose a personalized methodology for the frequency control problem, which combines long-term value optimization using reinforcement learning (RL) with a robust volume control technique we termed "Effective Factor". We demonstrate statistically significant improvement in daily metrics and resource efficiency by our method in several notification applications at a scale of billions of users. To our best knowledge, our study represents the first deep RL application on the frequency control problem at such an industrial scale.

Download the Paper

AUTHORS

Written by

Yang Liu (CPP)

Feng Liang

Jiahao Wu

Juan Wang

Kittipat Virochsiri

Zhengxing Chen

Publisher

The Thirty-Fifth AAAI Conference on Artificial Intelligence

Related Publications

February 15, 2024

RANKING AND RECOMMENDATIONS

CORE MACHINE LEARNING

TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning

Danny Deng, Hongkuan Zhou, Hanqing Zeng, Yinglong Xia, Chris Leung (AI), Jianbo Li, Rajgopal Kannan, Viktor Prasanna

February 15, 2024

January 06, 2024

RANKING AND RECOMMENDATIONS

REINFORCEMENT LEARNING

Learning to bid and rank together in recommendation systems

Geng Ji, Wentao Jiang, Jiang Li, Fahmid Morshed Fahid, Zhengxing Chen, Yinghua Li, Jun Xiao, Chongxi Bao, Zheqing (Bill) Zhu

January 06, 2024

December 11, 2023

REINFORCEMENT LEARNING

CORE MACHINE LEARNING

TaskMet: Task-driven Metric Learning for Model Learning

Dishank Bansal, Ricky Chen, Mustafa Mukadam, Brandon Amos

December 11, 2023

October 01, 2023

REINFORCEMENT LEARNING

CORE MACHINE LEARNING

Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots

Wei Hung, Bo-Kai Huang, Ping-Chun Hsieh, Xi Liu

October 01, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.