Framework para sistemas de recomendação baseados em neural contextual Bandits com restrição de justiça
Nenhuma Miniatura disponível
Data
2024-06-03
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Goiás
Resumo
The advent of digital businesses such as marketplaces, in which a company mediates a
commercial transaction between different actors, presents challenges to recommendation
systems as it is a multi-stakeholder scenario. In this scenario, the recommendation must
meet conflicting objectives between the parties, such as relevance versus exposure, for
example. State-of-the-art models that address the problem in a supervised way not only
assume that the recommendation is a stationary problem, but are also user-centered, which
leads to long-term system degradation. This thesis focuses on modeling the recommendation system as a reinforcement learning problem, through a Markovian decision-making
process with uncertainty where it is possible to model the different interests of stakeholders in an environment with fairness constraints. The main challenges are the need for real
interactions between stakeholders and the recommendation system in a continuous cycle
of events that enables the scenario for online learning. For the development of this work,
we present a model proposal, based on Neural Contextual Bandits with fairness constrain
for multi-stakeholder scenarios. As results, we present the construction of MARS-Gym,
a framework for modeling, training and evaluating recommendation systems based on
reinforcement learning, and the development of different recommendation policies with
fairness control adaptable to Neural models. Contextual Bandits, which led to an increase
in fairness metrics for all scenarios presented while controlling the reduction in relevance
metrics.
Descrição
Citação
SANTANA, M. R. O. Framework para Sistemas de Recomendação Baseados em Neural Contextual Bandits com Restrição de Justiça. Goiânia. 2024. 105p. Tese (Doutorado em Ciência da Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2024.