Subtractive random forests with two choices

Fuente: arXiv
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Autores principales: Calvillo, Francisco, Devroye, Luc, Lugosi, Gábor
Formato: Preprint
Publicado: 2024
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author Calvillo, Francisco
Devroye, Luc
Lugosi, Gábor
author_facet Calvillo, Francisco
Devroye, Luc
Lugosi, Gábor
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2405_10455
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Subtractive random forests with two choices
Calvillo, Francisco
Devroye, Luc
Lugosi, Gábor
Probability
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.
title Subtractive random forests with two choices
topic Probability
url https://arxiv.org/abs/2405.10455