Subtractive Random Forests with Two Choices
Abstract
Abstract 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.
Fields of Study & Subjects
Data Management and Algorithms
Bayesian Modeling and Causal Inference
Publication details
- Journal / source
- Methodology And Computing In Applied Probability
- Publisher
- Springer Science and Business Media LLC
- Volume
- 28
- Issue
- 2
- Publication date
- 4 May 2026
- Language
- EN