用大规模线性规划实现个性化推荐的高效探索与约束优化。
BanditLP: Large-Scale Stochastic Optimization for Personalized Recommendations
- 结合神经网络采样与大规模线性规划，动态选择最优推荐动作。
- 在公开数据集和合成数据上优于强基线，LinkedIn实测提升业务指标。
- 适合需要平衡探索与约束的工业级推荐系统部署场景。
我们提出BanditLP，一个可扩展的多利益相关方上下文博弈框架，将神经网络泰勒斯采样用于学习目标特定结果，并在服务时通过大规模线性规划实现受约束的动作选择。该方法与任意神经架构兼容，可部署于互联网规模，其线性规划求解器支持数十亿变量。在公共基准和合成数据上的实验表明，该方法持续优于强基线。我们将此方法应用于LinkedIn的邮件营销系统，验证了实际业务收益，展示了集成探索与约束优化在生产环境中的价值。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。