Subtractive Random Forests with Two Choices

• Authors: Francisco Calvillo, Luc P Devroye and Gábor Lugosi
• Methodology and Computing in Applied Probability, Vol. 28, No. 2, 49, June 2026

Recommendation systems are pivotal in aiding users amid vast online content. Broutin, Devroye, Lugosi, and Oliveira proposed Subtractive Random Forests (surf), a model that emphasizes temporal user preferences. Expanding on surf, we introduce a model for a multi-choice recommendation system, enabling users to select from two independent suggestions based on past interactions. We evaluate its effectiveness and robustness across diverse scenarios, incorporating heavy-tailed distributions for time delays. By analyzing user topic evolution, we assess the system’s consistency. Our study offers insights into the performance and potential enhancements of multi-choice recommendation systems in practical settings.