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About

I am a PostDoc Researcher at the Laboratory of Artificial Intelligence and Decision Support (LIAAD - INESC Tec), and an Invited Professor at the Sciences College of the University of Porto (FCUP). I successfully defended my Ph.D. at FCUP in 2017, under the supervision of Professor Luís Torgo. My work was fully funded by a scholarship awarded by FCT (Portuguese Foundation for Science and Technology), and my final dissertation was awarded in the Fraunhofer Portugal Challenge 2017. I obtained a MSc in Architectures, Systems and Networks at the Polytechnic Institute of Oporto (Oporto Engineering Superior Institute) in 2012 and was awarded a merit diploma for outstanding academic performance. My interests include outlier prediction, utility-based regression, imbalanced domains and social media analytics.

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Publications

2019

A review on web content popularity prediction: Issues and open challenges

Authors
Moniz, N; Torgo, L;

Publication
Online Social Networks and Media

Abstract
With the profusion of web content, researchers have avidly studied and proposed new approaches to enable the anticipation of its impact on social media, presenting many distinct approaches throughout the last decade. Diverse approaches have been presented to tackle the problem of web content popularity prediction, including standard classification and regression approaches. Furthermore, these approaches have also taken into consideration distinct scenarios of data availability, where one may target the prediction of popularity before or after the publication of the items, which is highly interesting for different objectives from a user standpoint. This work aims at reviewing previous work and discussing open issues and challenges that could foster impactful research on this topic. Five areas are identified that require further research, covering the full spectrum of the problem: social media data, the learning task, recommendation and evaluation. © 2019 Elsevier B.V.

2018

The Utility Problem of Web Content Popularity Prediction

Authors
Moniz, N; Torgo, L;

Publication
Proceedings of the 29th on Hypertext and Social Media, HT 2018, Baltimore, MD, USA, July 09-12, 2018

Abstract
The ability to generate and share content on social media platforms has changed the Internet. With the growing rate of content generation, efforts have been directed at making sense of such data. One of the most researched problem concerns predicting web content popularity. We argue that the evolution of state-of-the-art approaches has been optimized towards improving the predictability of average behaviour of data: items with low levels of popularity. We demonstrate this effect using a utility-based framework for evaluating numerical web content popularity prediction tasks, focusing on highly popular items. Additionally, it is demonstrated that gains in predictive and ranking ability of such type of cases can be obtained via naïve approaches, based on strategies to tackle imbalanced domains learning tasks. © 2018 Association for Computing Machinery.

2018

Constructive Aggregation and Its Application to Forecasting with Dynamic Ensembles

Authors
Cerqueira, V; Pinto, F; Torgo, L; Soares, C; Moniz, N;

Publication
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I

Abstract

2018

SMOTEBoost for Regression: Improving the Prediction of Extreme Values

Authors
Moniz, N; Ribeiro, RP; Cerqueira, V; Chawla, N;

Publication
5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018, Turin, Italy, October 1-3, 2018

Abstract

2018

Cost-Sensitive Learning: Preface

Authors
Torgo, L; Matwin, S; Weiss, G; Moniz, N; Branco, P;

Publication
International Workshop on Cost-Sensitive Learning, COST@SDM 2018, San Diego, California, USA, May 5, 2018

Abstract