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Founder's Pitch
""BanditLP: Optimize large-scale personalized recommendations with multi-stakeholder alignment and scalability in mind.""
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Breakdown pending for this paper.
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Why It Matters
This research addresses the need for efficient recommendation systems that maximize multi-stakeholder objectives under operational constraints, crucial for large-scale platforms like LinkedIn, which balance diverse needs in real-time interactions.
Product Angle
To productize BanditLP, integrate it as a feature in existing recommendation engines, offering enhanced capabilities for handling multi-objective constraints, scalable to user bases of platforms like LinkedIn.
Disruption
BanditLP could replace less sophisticated recommendation systems that struggle to manage multiple objectives and constraints, potentially opening new performance heights for large-scale platforms.
Product Opportunity
The demand for advanced recommendation systems in marketing and e-commerce is substantial, driven by the need for personalized content delivery and multi-objective optimization. Enterprises and advertisers will pay for systems that improve engagement while respecting diverse objectives.
Use Case Idea
Deploy BanditLP in email marketing platforms or online marketplaces like Amazon to optimize user engagement and advertiser objectives while respecting operational limits and fairness constraints.
Science
BanditLP combines neural Thompson sampling with large-scale linear programming, allowing for exploration-exploitation with constraints. It leverages neural networks to model rewards and costs, optimizing recommendations using LP solvers that manage billions of variables, integrating diverse stakeholder needs.
Method & Eval
It was tested on LinkedIn's email marketing and synthetic data, outperforming current baseline models through real-world business improvements demonstrated via A/B testing and online experiments.
Caveats
Reliability on synthetic benchmarks might not entirely capture real-world complexity. Its scalability is theoretically defined and tested in LinkedIn's context, but might encounter unforeseen issues elsewhere.
Author Intelligence
Phuc Nguyen
LinkedIn
honnguyen@linkedin.com
Benjamin Zelditch
LinkedIn
bzelditch@linkedin.com
Joyce Chen
LinkedIn
joychen@linkedin.com
Rohit Patra
LinkedIn
ropatra@linkedin.com
Changshuai Wei
LinkedIn
chawei@linkedin.com