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Sobre

Sobre

Rui Jorge Ramos, investigador e full stack software developer no centro de Computação Centrada no Humano e Ciência da Informação (HumanISE). Apaixonado pela ciência, pela matemática e pelo poder dos dados. A sua atividade no INESC TEC centra-se em desenvolvimento de software e investigação nas áreas da privacidade e segurança dos dados, machine learning e ciência de dados, com especial foco em temas como a saúde, a terra e o oceano. Assistente Convidado na Faculdade de Engenharia da Universidade do Porto. Estudante do Doutoramento em Ciência de Computadores na Faculdade de Ciências da Universidade do Porto, com investigação orientada a machine learning distribuída e privacidade e segurança de dados.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Rui Jorge Ramos
  • Cargo

    Investigador
  • Desde

    26 fevereiro 2020
005
Publicações

2023

Hybrid SkipAwareRec: A Streaming Music Recommendation System

Autores
Ramos, R; Oliveira, L; Vinagre, J;

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT I

Abstract
In an automatic music playlist generator, such as an automated online radio channel, how should the system react when a user hits the skip button? Can we use this type of negative feedback to improve the list of songs we will playback for the user next? We propose SkipAwareRec, a next-item recommendation system based on reinforcement learning. SkipAwareRec recommends the best next music categories, considering positive feedback consisting of normal listening behaviour, and negative feedback in the form of song skips. Since SkipAwareRec recommends broad categories, it needs to be coupled with a model able to choose the best individual items. To do this, we propose Hybrid SkipAwareRec. This hybrid model combines the SkipAwareRec with an incremental Matrix Factorisation (MF) algorithm that selects specific songs within the recommended categories. Our experiments with Spotify's Sequential Skip Prediction Challenge dataset show that Hybrid SkipAwareRec has the potential to improve recommendations by a considerable amount with respect to the skip-agnostic MF algorithm. This strongly suggests that reformulating the next recommendations based on skips improves the quality of automatic playlists. Although in this work we focus on sequential music recommendation, our proposal can be applied to other sequential content recommendation domains, such as health for user engagement.

2022

Digital Twin for Monitoring Containerized Hazmat Cargo in Port Areas

Autores
Oliveira, L; Castro, M; Ramos, R; Santos, J; Silva, J; Dias, L;

Publicação
2022 17TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI)

Abstract
The complexity of the number of stakeholders, information systems used, and port operations evoke new challenges to port security when it comes to the total knowledge and control of the overall operations of transport and parking of containerized freight, namely hazmat ones. The rising interest and the port authorities' awareness of the relevance of security concerns involved in this complex ecosystem has led to the search for new technological solutions that allow, in an integrated manner, the smart and automatic control of operations of transport and hazardous freight parking in all the areas of its jurisdiction, without third-party dependencies. Despite its importance and criticality, port authorities tend to have limited real-time knowledge of the location of hazmat containers, whether moving within the port (entering and leaving), or in its parking, having a direct impact on the port security. This article presents a Digital Twin platform for 3D and real-time georeferenced visualization of container parks and the location of hazardous containerized freight. This tool combines different modules that further allow to visualize information associated to a container, its movement, as well as its surrounding area, including a realistic and dynamic 3D representation of what is the area encircling the port.