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