RANKING AND RECOMMENDATIONS

REINFORCEMENT LEARNING

Learning to bid and rank together in recommendation systems

January 06, 2024

Abstract

Many Internet applications adopt real-time bidding mechanisms to ensure different services (types of content) are shown to the users through fair competitions. The service offering the highest bid price gets the content slot to present a list of items in its candidate pool. Through user interactions with the recommended items, the service obtains the desired engagement activities. We propose a contextual-bandit framework to jointly optimize the price to bid for the slot and the order to rank its candidates for a given service in this type of recommendation systems. Our method can take as input any feature that describes the user and the candidates, including the outputs of other machine learning models. We train reinforcement learning policies using deep neural networks, and compute top-K Gaussian propensity scores to exclude the variance in the gradients caused by randomness unrelated to the reward. This setup further facilitates us to automatically find accurate reward functions that trade off between budget spending and user engagements. In online A/B experiments on two major services of Facebook Home Feed, Groups You Should Join and Friend Requests, our method statistically significantly boosted the number of groups joined by 14.7%, the number of friend requests accepted by 7.0%, and the number of daily active Facebook users by about 1 million, against strong hand-tuned baselines that have been iterated in production over years.

Download the Paper

AUTHORS

Written by

Geng Ji

Wentao Jiang

Jiang Li

Fahmid Morshed Fahid

Zhengxing Chen

Yinghua Li

Jun Xiao

Chongxi Bao

Zheqing (Bill) Zhu

Publisher

Machine Learning

Research Topics

Ranking & Recommendations

Reinforcement Learning

Core Machine Learning

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

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

September 12, 2023

RANKING AND RECOMMENDATIONS

REINFORCEMENT LEARNING

Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning

Bill Zhu, Alex Nikulkov, Dmytro Korenkevych, Fan Liu, Jalaj Bhandari, Ruiyang Xu, Urun Dogan

September 12, 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.