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About

About

Bruno Veloso holds a M.Sc. in Electrical and Computers Engineering, Major in Telecommunications from the Polytechnic Institute of Porto (School of Engineering) and a Ph.D. degree in Telematics from the University of Vigo, Spain. He is a researcher at the INESC TEC (Laboratory of Artificial Intelligence and Decision Support (LIAAD)). His interests include distributed artificial intelligence, multi-agent systems, personalisation, recommendation systems and data streams.

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Details

Details

  • Name

    Bruno Miguel Veloso
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st March 2013
002
Publications

2019

On-line guest profiling and hotel recommendation

Authors
Veloso, BM; Leal, F; Malheiro, B; Carlos Burguillo, JC;

Publication
Electronic Commerce Research and Applications

Abstract

2019

Scalable Modelling and Recommendation using Wiki-based Crowdsourced Repositories

Authors
Leal, F; Veloso, BM; Malheiro, B; Gonzalez Velez, H; Carlos Burguillo, JC;

Publication
Electronic Commerce Research and Applications

Abstract

2019

Stream recommendation using individual hyper-parameters

Authors
Veloso, B; Malheiro, B; Foss, JD;

Publication
CEUR Workshop Proceedings

Abstract
Nowadays, with the widely usage of on-line stream video platforms, the number of media resources available and the volume of crowd-sourced feedback volunteered by viewers is increasing exponentially. In this scenario, the adoption of recommendation systems allows platforms to match viewers with resources. However, due to the sheer size of the data and the pace of the arriving data, there is the need to adopt stream mining algorithms to build and maintain models of the viewer preferences as well as to make timely personalised recommendations. In this paper, we propose the adoption of optimal individual hyper-parameters to build more accurate dynamic viewer models. First, we use a grid search algorithm to identify the optimal individual hyper-parameters (IHP) and, then, use these hyper-parameters to update incrementally the user model. This technique is based on an incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations. © 2019 for this paper by its authors.

2018

APASail—An Agent-Based Platform for Autonomous Sailing Research and Competition

Authors
Alves, B; Veloso, B; Malheiro, B;

Publication
Robotic Sailing 2017

Abstract

2018

Personalised Dynamic Viewer Profiling for Streamed Data

Authors
Veloso, B; Malheiro, B; Burguillo, JC; Foss, JD; Gama, J;

Publication
Advances in Intelligent Systems and Computing - Trends and Advances in Information Systems and Technologies

Abstract