Representation Curriculum: New Training Method Improves Ranking Systems' Fairness and Cold-Start Performance
Researchers propose Representation Curriculum (RC), a training technique that helps ranking systems in digital marketplaces rely less on historical popularity signals and more on content quality. The method addresses a fundamental problem where ranking algorithms become overly dependent on exposure-confounded signals (like click-through rates), which can entrench dominant sellers and harm new entrants. The approach shows measurable improvements in cold-start scenarios across e-commerce search systems while maintaining controlled trade-offs in overall performance.
A new machine learning technique called Representation Curriculum aims to improve how digital marketplaces rank and allocate visibility to items. The core problem is that modern ranking systems heavily rely on exposure-dependent signals like popularity metrics and click-through rates because they are highly predictive under normal conditions. However, this creates a learning shortcut where algorithms become over-reliant on historical signals, entrenching incumbent sellers and making it harder for new items to gain visibility. The proposed RC method stages feature utilization during training: it initially emphasizes content-based merit signals (like product quality and relevance), then gradually introduces historical belief signals while anchoring them to the learned merit representation. The researchers provide theoretical analysis in a Gaussian linear ridge setting and demonstrate the approach through experiments on public benchmarks and randomized online tests at a large-scale e-commerce platform, showing consistent gains for cold-start items with controlled trade-offs in head performance.
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- arXiv cs.LGCenter
Representation Curriculum: Stagewise Training for Robust Ranking and Allocation
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