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Publicações

Publicações por Nuno Moniz

2014

Resampling Approaches to Improve News Importance Prediction

Autores
Moniz, N; Torgo, L; Rodrigues, F;

Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XIII

Abstract
The methods used to produce news rankings by recommender systems are not public and it is unclear if they reflect the real importance assigned by readers. We address the task of trying to forecast the number of times a news item will be tweeted, as a proxy for the importance assigned by its readers. We focus on methods for accurately forecasting which news will have a high number of tweets as these are the key for accurate recommendations. This type of news is rare and this creates difficulties to standard prediction methods. Recent research has shown that most models will fail on tasks where the goal is accuracy on a small sub-set of rare values of the target variable. In order to overcome this, resampling approaches with several methods for handling imbalanced regression tasks were tested in our domain. This paper describes and discusses the results of these experimental comparisons.

2016

Resampling Strategies for Imbalanced Time Series

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

Publicação
PROCEEDINGS OF 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, (DSAA 2016)

Abstract
Time series forecasting is a challenging task, where the non-stationary characteristics of the data portrays a hard setting for predictive tasks. A common issue is the imbalanced distribution of the target variable, where some intervals are very important to the user but severely underrepresented. Standard regression tools focus on the average behaviour of the data. However, the objective is the opposite in many forecasting tasks involving time series: predicting rare values. A common solution to forecasting tasks with imbalanced data is the use of resampling strategies, which operate on the learning data by changing its distribution in favor of a given bias. The objective of this paper is to provide solutions capable of significantly improving the predictive accuracy of rare cases in forecasting tasks using imbalanced time series data. We extend the application of resampling strategies to the time series context and introduce the concept of temporal and relevance bias in the case selection process of such strategies, presenting new proposals. We evaluate the results of standard regression tools and the use of resampling strategies, with and without bias over 24 time series data sets from 6 different sources. Results show a significant increase in predictive accuracy of rare cases associated with the use of resampling strategies, and the use of biased strategies.

2016

Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks

Autores
Eirinaki, M; Moniz, N; Potika, K;

Publicação
PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCES ON BIG DATA AND CLOUD COMPUTING (BDCLOUD 2016) SOCIAL COMPUTING AND NETWORKING (SOCIALCOM 2016) SUSTAINABLE COMPUTING AND COMMUNICATIONS (SUSTAINCOM 2016) (BDCLOUD-SOCIALCOM-SUSTAINCOM 2016)

Abstract
The process of decision making in humans involves a combination of the genuine information held by the individual, and the external influence from their social network connections. This helps individuals to make decisions or adopt behaviors, opinions or products. In this work, we seek to investigate under which conditions and with what cost we can form neighborhoods of influence within a social network, in order to assist individuals with little or no prior genuine information through a two-phase recommendation process. Most of the existing approaches regard the problem of identifying influentials as a long-term, network diffusion process, where information cascading occurs in several rounds and has fixed number of influentials. In our approach we consider only one round of influence, which finds applications in settings where timely influence is vital. We tackle the problem by proposing a two-phase framework that aims at identifying influentials in the first phase and form influential neighborhoods to generate recommendations to users with no prior knowledge in the second phase. The difference of the proposed framework with most social recommender systems is that we need to generate recommendations including more than one item and in the absence of explicit ratings, solely relying on the social network's graph.

2015

Socially Driven News Recommendation

Autores
Moniz, Nuno; Torgo, Luis;

Publicação
CoRR

Abstract

2016

Relational Data on Members of Portuguese Governments (1976–2014)

Autores
Moniz, N; Campos, A;

Publicação
Data

Abstract

2015

Representatives and dominants: Rulers and class relations in Portugal [Representantes e dominantes: Os governantes e as relações de classe em Portugal]

Autores
Campos, A; Costa, J; Lopes, JT; Louca, F; Moniz, N;

Publicação
Revista Critica de Ciencias Sociais

Abstract
This article discusses the relationships established between capital owners and the groups of rulers and former rulers, embracing a critical perspective capable of enhancing the State's role in the definition of economic power. Special attention is given to the cooptation process, an analysis that includes data on 776 rulers who occupied 1281 positions in the 19 constitutional governments (1976-2014).

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