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About

Benedita Malheiro holds a PhD. and a MSc. in Electrical Engineering and Computers and a five year graduation degree in Electrical Engineering from the University of Porto (Faculty of Engineering). She is an Adjunct Professor at the Electrical Engineering Department of the Instituto Superior de Engenharia do Porto (ISEP) where she is responsible for two modules of the MSc. in Electrical and Computers Engineering degree and supervises undergraduate (Final Project/Internship) and postgraduate (Thesis/Dissertation) students. As a researcher she is specialized in problems of a distributed, dynamic and decentralized nature.

Interest
Topics
Details

Details

002
Publications

2020

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

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

Publication
Electronic Commerce Research and Applications

Abstract

2020

Airfoil Selection and Wingsail Design for an Autonomous Sailboat

Authors
Silva, MF; Malheiro, B; Guedes, P; Ferreira, P;

Publication
Advances in Intelligent Systems and Computing - Robot 2019: Fourth Iberian Robotics Conference

Abstract

2020

Smart Companion Pillow – An EPS@ISEP 2019 Project

Authors
dos Reis, AS; Gielen, E; Wopereis, K; Pasternak, M; Sooäär, V; Schneider, T; Duarte, AJ; Malheiro, B; Justo, J; Ribeiro, C; Silva, MF; Ferreira, P; Guedes, P;

Publication
Advances in Intelligent Systems and Computing - Robot 2019: Fourth Iberian Robotics Conference

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; González Vélez, H; Carlos Burguillo, J;

Publication
Electronic Commerce Research and Applications

Abstract

2019

Analysis and prediction of hotel ratings from crowdsourced data

Authors
Leal, F; Malheiro, B; Burguillo, JC;

Publication
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Abstract
Crowdsourcing has become an essential source of information for tourism stakeholders. Every day, tourists leave large volumes of feedback data in the form of posts, likes, textual reviews, and ratings in dedicated crowdsourcing platforms. This behavior makes the analysis of crowdsourced information strategic, allowing the discovery of important knowledge regarding tourists and tourism resources. This paper presents a survey on the analysis and prediction of hotel ratings from crowdsourced data, covering both off-line (batch) and on-line (stream-based) processing. Specifically, it reports multiple rating-based profiling, recommendation, and evaluation techniques. While most of the surveyed works adopt entity-based multicriteria profiling, prerecommendation filtering, and off-line processing, the latest hotel rating prediction trends include feature-based, trust and reputation modeling, postrecommendation filtering, and on-line processing. Additionally, since the volume of crowdsourced ratings tends to increase, the deployment of profiling and recommendation algorithms on high-performance computing resources should be further explored. This article is categorized under: Application Areas > Internet and Web-Based Applications. © 2018 Wiley Periodicals, Inc.

Supervised
thesis

2018

O Impacto da Automatização e Inteligência Artificial nos Serviços Partilhados

Author
João Francisco de Magalhães e Silva

Institution
UP-FEUP

2017

Media Content Personalisation Brokerage Platform

Author
Bruno Miguel Delindro Veloso

Institution
Outra

2016

Plataforma de Competição de Veleiros Autónomos

Author
BRUNO MIGUEL FERREIRA ALVES

Institution
IPP-ISEP

2016

Renegociação de Contratos de Intermediação da Comercialização Eletrónica de Recursos de Cloud Computing

Author
RÚBEN DE CASTRO RODRIGUES MOREIRA DA CUNHA

Institution
IPP-ISEP

2016

Automatic Fluid Sampler

Author
PEPIJN DE WINTER

Institution
IPP-ISEP