Scaling up Ranking under Constraints for Live Recommendations by Replacing Optimization with Prediction

TL;DR

This paper introduces a prediction-based approach to efficiently solve large-scale, real-time constrained ranking problems, significantly reducing computational resources while maintaining high constraint compliance and utility.

Contribution

It replaces traditional optimization with a prediction method, enabling faster, scalable, and resource-efficient constrained ranking solutions for live recommendation systems.

Findings

Major reduction in computational resources needed

Maintains high constraint compliance and utility

Achieves real-time performance within 50 milliseconds

Abstract

Many important multiple-objective decision problems can be cast within the framework of ranking under constraints and solved via a weighted bipartite matching linear program. Some of these optimization problems, such as personalized content recommendations, may need to be solved in real time and thus must comply with strict time requirements to prevent the perception of latency by consumers. Classical linear programming is too computationally inefficient for such settings. We propose a novel approach to scale up ranking under constraints by replacing the weighted bipartite matching optimization with a prediction problem in the algorithm deployment stage. We show empirically that the proposed approximate solution to the ranking problem leads to a major reduction in required computing resources without much sacrifice in constraint compliance and achieved utility, allowing us to solve larger constrained ranking problems real-time, within the required 50 milliseconds, than previously reported.