BanditLP: Large-Scale Stochastic Optimization for Personalized Recommendations

TL;DR

BanditLP is a scalable, application-agnostic framework combining neural Thompson Sampling with large-scale linear programming to optimize personalized recommendations efficiently at web scale, demonstrating significant real-world business benefits.

Contribution

The paper introduces BanditLP, a novel framework unifying neural Thompson Sampling with large-scale linear programming for constrained, personalized decision-making at scale.

Findings

Consistent performance improvements over strong baselines on benchmarks.

Successful deployment in LinkedIn's email marketing system.

Demonstrated business value through real-world application.

Abstract

We present BanditLP, a scalable multi-stakeholder contextual bandit framework that unifies neural Thompson Sampling for learning objective-specific outcomes with a large-scale linear program for constrained action selection at serving time. The methodology is application-agnostic, compatible with arbitrary neural architectures, and deployable at web scale, with an LP solver capable of handling billions of variables. Experiments on public benchmarks and synthetic data show consistent gains over strong baselines. We apply this approach in LinkedIn's email marketing system and demonstrate business win, illustrating the value of integrated exploration and constrained optimization in production.