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Leveraging consensus effect to optimize ranking in online discussion boards
Gad Allon1, Joseph Carlstein1, Yonatan Gur2
1University of Pennsylvania, The Wharton School; 2Stanford Graduate School of Business
Online discussion platforms facilitate remote discussions between users. This paper explores the impact of consensus on engagement and proposes algorithms to optimize rankings. Consensus is identified as a crucial engagement driver, and our proposed algorithm outperformed current approaches in an experiment. Promoting debate over echo chambers, consensus is essential for user engagement and platform design.
Pooling and boosting for demand prediction in retail: a transfer learning approach
1Tsinghua University; 2JD.com Smart Supply Chain Y; 3University of Toronto; 4University of California, Berkeley
Retailers use our framework to leverage category sales data for individual product demand prediction. Integrating category-product information, we exploit risk pooling through transfer learning. Our approach combines data from different levels, treating top-level sales as regularization. It outperforms JD.com benchmarks by over 9%, highlighting the value of transfer learning in demand prediction for cost savings in low-margin e-retail.
Got (optimal) milk? Pooling donations in human milk banks with machine learning and optimization
Human donor milk is vital for preterm infants, but its macronutrient content varies, necessitating pooling. To address resource limitations in milk banks, we propose a data-driven framework using machine learning and optimization. Collaborating with a milk bank, we collect data, fine-tune models, and simulate operational scenarios. Our approach improves macronutrient target achievement by 31-76% and reduces recipe creation time by 67% compared to baselines.
