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.
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.
2019
Authors
Veloso, BM; Leal, F; Malheiro, B; Carlos Burguillo, JC;
Publication
Electronic Commerce Research and Applications
Abstract
2019
Authors
Leal, F; Veloso, BM; Malheiro, B; Gonzalez Velez, H; Carlos Burguillo, JC;
Publication
Electronic Commerce Research and Applications
Abstract
2019
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.
2019
Authors
Veloso, B; Leal, F; Malheiro, B; Moreira, F;
Publication
Procedia Computer Science
Abstract
2018
Authors
Alves, B; Veloso, B; Malheiro, B;
Publication
Robotic Sailing 2017
Abstract
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