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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.

Interest
Topics
Details

Details

  • Name

    Bruno Miguel Veloso
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st March 2013
001
Publications

2020

A 2020 perspective on “Online guest profiling and hotel recommendation”

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

Publication
Electronic Commerce Research and Applications

Abstract

2020

A 2020 perspective on “Scalable modelling and recommendation using wiki-based crowdsourced repositories:” Fairness, scalability, and real-time recommendation

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

Publication
Electronic Commerce Research and Applications

Abstract

2020

On fast and scalable recurring link's prediction in evolving multi-graph streams

Authors
Tabassum, S; Veloso, B; Gama, J;

Publication
Network Science

Abstract
The link prediction task has found numerous applications in real-world scenarios. However, in most of the cases like interactions, purchases, mobility, etc., links can re-occur again and again across time. As a result, the data being generated is excessively large to handle, associated with the complexity and sparsity of networks. Therefore, we propose a very fast, memory-less, and dynamic sampling-based method for predicting recurring links for a successive future point in time. This method works by biasing the links exponentially based on their time of occurrence, frequency, and stability. To evaluate the efficiency of our method, we carried out rigorous experiments with massive real-world graph streams. Our empirical results show that the proposed method outperforms the state-of-the-art method for recurring links prediction. Additionally, we also empirically analyzed the evolution of links with the perspective of multi-graph topology and their recurrence probability over time. © 2020 Cambridge University Press.

2020

Fraud detection using heavy hitters: A case study

Authors
Veloso, B; Martins, C; Espanha, R; Azevedo, R; Gama, J;

Publication
Proceedings of the ACM Symposium on Applied Computing

Abstract
The high asymmetry of international termination rates, where calls are charged with higher values, are fertile ground for the appearance of frauds in Telecom Companies. In this paper, we present three different and complementary solutions for a real problem called Interconnect Bypass Fraud. This problem is one of the most common in the telecommunication domain and can be detected by the occurrence of abnormal behaviours from specific numbers. Our goal is to detect as soon as possible numbers with abnormal behaviours, e.g. bursts of calls, repetitions and mirror behaviours. Based on this assumption, we propose: (i) the adoption of a new fast forgetting technique that works together with the Lossy Counting algorithm; (ii) the proposal of a single pass hierarchical heavy hitter algorithm that also contains a forgetting technique; and (iii) the application of the HyperLogLog sketches for each phone number. We used the heavy hitters to detect abnormal behaviours, e.g. burst of calls, repetition and mirror. The hierarchical heavy hitters algorithm is used to detect the numbers that make calls for a huge set of destinations and destination numbers that receives a huge set of calls to provoke a denial of service. Additionally, to detect the cardinality of destination numbers of each origin number we use the HyperLogLog algorithm. The results shows that these three approaches combined complements the techniques used by the telecom company and make the fraud task more difficult. © 2020 ACM.

2020

Trust and Reputation Smart Contracts for Explainable Recommendations

Authors
Leal, F; Veloso, B; Malheiro, B; Vélez, HG;

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

Abstract