Marlesson R. O. Santana

Marlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo et al.

Recommender Systems are especially challenging for marketplaces since they must maximize user satisfaction while maintaining the healthiness and fairness of such ecosystems. In this context, we observed a lack of resources to design, train, and evaluate agents that learn by interacting within these environments. For this matter, we propose MARS-Gym, an open-source framework to empower researchers and engineers to quickly build and evaluate Reinforcement Learning agents for recommendations in marketplaces. MARS-Gym addresses the whole development pipeline: data processing, model design and optimization, and multi-sided evaluation. We also provide the implementation of a diverse set of baseline agents, with a metrics-driven analysis of them in the Trivago marketplace dataset, to illustrate how to conduct a holistic assessment using the available metrics of recommendation, off-policy estimation, and fairness. With MARS-Gym, we expect to bridge the gap between academic research and production systems, as well as to facilitate the design of new algorithms and applications.

Gustavo Polleti, Marlesson Santana, Eduardo Fontes

We introduced a multimodal foundational model for financial transactions that integrates both structured attributes and unstructured textual descriptions into a unified representation. By adapting masked language modeling to transaction sequences, we demonstrated that our approach not only outperforms classical feature engineering and discrete event sequence methods but is also particularly effective in data-scarce Open Banking scenarios. To our knowledge, this is the first large-scale study across thousands of financial institutions in North America, providing evidence that multimodal representations can generalize across geographies and institutions. These results highlight the potential of self-supervised models to advance financial applications ranging from fraud prevention and credit risk to customer insights

Gustavo Polleti, Marlesson Santana, Felipe Sassi Del Sant et al.

Open Banking powered machine learning applications require novel robustness approaches to deal with challenging stress and failure scenarios. In this paper we propose an hierarchical fallback architecture for improving robustness in high risk machine learning applications with a focus in the financial domain. We define generic failure scenarios often found in online inference that depend on external data providers and we describe in detail how to apply the hierarchical fallback architecture to address them. Finally, we offer a real world example of its applicability in the industry for near-real time transactional fraud risk evaluation using Open Banking data and under extreme stress scenarios.

Marlesson R. O. Santana, Anderson Soares

Deep learning based methods have been used successfully in recommender system problems. Approaches using recurrent neural networks, transformers, and attention mechanisms are useful to model users' long- and short-term preferences in sequential interactions. To explore different session-based recommendation solutions, Booking.com recently organized the WSDM WebTour 2021 Challenge, which aims to benchmark models to recommend the final city in a trip. This study presents our approach to this challenge. We conducted several experiments to test different state-of-the-art deep learning architectures for recommender systems. Further, we proposed some changes to Neural Attentive Recommendation Machine (NARM), adapted its architecture for the challenge objective, and implemented training approaches that can be used in any session-based model to improve accuracy. Our experimental result shows that the improved NARM outperforms all other state-of-the-art benchmark methods.