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Learning to bid and rank together in recommendation systems.

Authors :
Ji, Geng
Jiang, Wentao
Li, Jiang
Fahid, Fahmid Morshed
Chen, Zhengxing
Li, Yinghua
Xiao, Jun
Bao, Chongxi
Zhu, Zheqing
Source :
Machine Learning; May2024, Vol. 113 Issue 5, p2559-2573, 15p
Publication Year :
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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08856125
Volume :
113
Issue :
5
Database :
Complementary Index
Journal :
Machine Learning
Publication Type :
Academic Journal
Accession number :
176997847
Full Text :
https://doi.org/10.1007/s10994-023-06444-4