Subtractive random forests with two choices

Recommendation systems are pivotal in aiding users amid vast online content. Broutin, Devroye, Lugosi, and Oliveira proposed Subtractive Random Forests ($\text{textsc}surf$), a model that emphasizes temporal user preferences. Expanding on $\text{textsc}surf$, 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.