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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

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.

2017

A Framework for Recommendation of Highly Popular News Lacking Social Feedback

Authors
Moniz, N; Torgo, L; Eirinaki, M; Branco, P;

Publication
NEW GENERATION COMPUTING

Abstract
Social media is rapidly becoming the main source of news consumption for users, raising significant challenges to news aggregation and recommendation tasks. One of these challenges concerns the recommendation of very recent news. To tackle this problem, approaches to the prediction of news popularity have been proposed. In this paper, we study the task of predicting news popularity upon their publication, when social feedback is unavailable or scarce, and to use such predictions to produce news rankings. Unlike previous work, we focus on accurately predicting highly popular news. Such cases are rare, causing known issues for standard prediction models and evaluation metrics. To overcome such issues we propose the use of resampling strategies to bias learners towards these rare cases of highly popular news, and a utility-based framework for evaluating their performance. An experimental evaluation is performed using real-world data to test our proposal in distinct scenarios. Results show that our proposed approaches improve the ability of predicting and recommending highly popular news upon publication, in comparison to previous work.

2017

Learning with Imbalanced Domains: Preface

Authors
Torgo, L; Krawczyk, B; Branco, P; Moniz, N;

Publication
First International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA@PKDD/ECML 2017, 22 September 2017, Skopje, Macedonia

Abstract

2017

Evaluation of Ensemble Methods in Imbalanced Regression Tasks

Authors
Moniz, N; Branco, P; Torgo, L;

Publication
First International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA@PKDD/ECML 2017, 22 September 2017, Skopje, Macedonia

Abstract

2017

Resampling strategies for imbalanced time series forecasting

Authors
Moniz, Nuno; Branco, Paula; Torgo, Luis;

Publication
I. J. Data Science and Analytics

Abstract