The Power of Linear Programming in Sponsored Listings Ranking: Evidence from a Large-Scale Field Experiment

Abstract

Sponsored product advertisements constitute a major revenue source for online marketplaces such as Amazon, Walmart, and Alibaba. A key operational challenge in these systems lies in the Sponsored Listings Ranking (SLR) problem, that is, determining which items to include and how to rank them to balance short-term revenue with long-term relevance and user experience. Industry practice predominantly relies on score-based algorithms, which construct heuristic composite scores to rank items efficiently within strict real-time latency constraints. However, such methods offer limited control over objective trade-offs and cannot readily accommodate additional operational constraints. We propose and evaluate a Linear Programming (LP)-based algorithm as a principled alternative to score-based approaches. We first formulate the SLR problem as a constrained mixed integer programming (MIP) model and develop a dual-based algorithm that approximately solves its LP relaxation within 0.1 second, satisfying production-level latency requirements. In collaboration with a leading online marketplace, we conduct a 19-day field experiment encompassing approximately 329 million impressions. The LP-based algorithm significantly outperforms the industry-standard benchmark in key marketplace metrics, demonstrating both higher revenue and maintained relevance. Mechanism analyses reveal that the performance gains are most pronounced when the revenue-relevance tradeoff is stronger. Our framework also generalizes to settings with inventory, sales, or fairness constraints, offering a flexible and deployable optimization paradigm. The LP-based algorithm was deployed in production at our partner marketplace in January 2023, marking a rare large-scale implementation of a mathematically grounded ranking algorithm in real-world online advertising.