Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
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.

Interest
Topics
Details

Details

  • Name

    Bruno Miguel Veloso
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st March 2013
Publications

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

2018

Scalable data analytics using crowdsourced repositories and streams

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

Publication
Journal of Parallel and Distributed Computing

Abstract
The scalable analysis of crowdsourced data repositories and streams has quickly become a critical experimental asset in multiple fields. It enables the systematic aggregation of otherwise disperse data sources and their efficient processing using significant amounts of computational resources. However, the considerable amount of crowdsourced social data and the numerous criteria to observe can limit analytical off-line and on-line processing due to the intrinsic computational complexity. This paper demonstrates the efficient parallelisation of profiling and recommendation algorithms using tourism crowdsourced data repositories and streams. Using the Yelp data set for restaurants, we have explored two different profiling approaches: entity-based and feature-based using ratings, comments, and location. Concerning recommendation, we use a collaborative recommendation filter employing singular value decomposition with stochastic gradient descent (SVD-SGD). To accurately compute the final recommendations, we have applied post-recommendation filters based on venue suitability, value for money, and sentiment. Additionally, we have built a social graph for enrichment. Our master–worker implementation shows super-linear scalability for 10, 20, 30, 40, 50, and 60 concurrent instances. © 2018 Elsevier Inc.

2018

Self Hyper-Parameter Tuning for Data Streams

Authors
Veloso, B; Gama, J; Malheiro, B;

Publication
Discovery Science - Lecture Notes in Computer Science

Abstract

2018

Scalable Modelling and Recommendation using Wiki-based Crowdsourced Repositories

Authors
Leal, F; Veloso, BM; Malheiro, B; González–Vélez, H; Burguillo, JC;

Publication
Electronic Commerce Research and Applications

Abstract