Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Interest
Topics
Details

Details

Publications

2020

Ten Years Exploring Service Science: Looking Back to Move Forward

Authors
Teixeira, JG; Miguéis, V; Ferreira, MC; Nóvoa, H; Cunha, JF;

Publication
Lecture Notes in Business Information Processing

Abstract
In celebration of the 10th anniversary of the International Conference on Exploring Service Science (IESS), this paper takes a historical look at the papers that have been published in the IESS proceedings. The analysis is focused on the development and evolution of the IESS community and of the main research topics covered by the published papers over time. The IESS community is portrayed in terms of authors, their affiliations and co-authoring network, while the topics are analyzed according to the papers’ keywords. Moreover, this paper analyzes the impact of the papers published in this decade, in terms of citations. These results are then discussed in light of the observed trends and of the evolution of the service science field, to guide the future development of the IESS conference and of research on service science. © Springer Nature Switzerland AG 2020.

2019

A Data Mining Approach for Predicting Academic Success – A Case Study

Authors
Martins, MPG; Miguéis, VL; Fonseca, DSB; Alves, A;

Publication
Advances in Intelligent Systems and Computing

Abstract
The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure. © 2019, Springer Nature Switzerland AG.

2019

Evaluating the short-term effect of cross-market discounts in purchases using neural networks: A case in retail sector

Authors
Migueis, VL; Camanho, AS; Falcao e Cunha, JFE;

Publication
Expert Systems

Abstract
Promotional tools such as cross-market discounts have been increasingly used as a means to increase customer satisfaction and sales. This paper aims to assess whether the implementation of a cross-market discount campaign by a retailing company encouraged customers to increase their purchases level. It contributes to the literature by using neural networks to detect novelties in a real context involving cross-market discounts. Besides the computation of point predictions, the methodology proposed involves the estimation of neural networks prediction intervals. Sales predictions are compared with the observed values in order to detect significant changes in customers' spending. The use of neural networks is validated through the comparison with the forecasting estimates of support vector regression, regression trees, and linear regression. The results reveal that the promotional campaign under analysis did not significantly impact the sales of the rewarded customers. © 2019 John Wiley & Sons, Ltd.

2018

Exploring the use of deep neural networks for sales forecasting in fashion retail

Authors
Loureiro, ALD; Migueis, VL; da Silva, LFM;

Publication
DECISION SUPPORT SYSTEMS

Abstract
In the increasingly competitive fashion retail industry, companies are constantly adopting strategies focused on adjusting the products characteristics to closely satisfy customers' requirements and preferences. Although the lifecycles of fashion products are very short, the definition of inventory and purchasing strategies can be supported by the large amounts of historical data which are collected and stored in companies' databases. This study explores the use of a deep learning approach to forecast sales in fashion industry, predicting the sales of new individual products in future seasons. This study aims to support a fashion retail company in its purchasing operations and consequently the dataset under analysis is a real dataset provided by this company. The models were developed considering a wide and diverse set of variables, namely products' physical characteristics and the opinion of domain experts. Furthermore, this study compares the sales predictions obtained with the deep learning approach with those obtained with a set of shallow techniques, i.e. Decision Trees, Random Forest, Support Vector Regression, Artificial Neural Networks and Linear Regression. The model employing deep learning was found to have good performance to predict sales in fashion retail market, however for part of the evaluation metrics considered, it does not perform significantly better than some of the shallow techniques, namely Random Forest.

2018

A Data Mining Approach to Predict Undergraduate Students' Performance

Authors
Martins, MPG; Migueis, VL; Fonseca, DSB;

Publication
2018 13TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI)

Abstract
This paper presents a methodology based on random forest algorithm to predict the undergraduate academic performance of students from a polytechnic institution. The approach followed enabled to select 11 explanatory variables, starting from an initial set of around fifty, which allow to obtain a good predictive performance (R-2=0.79). These variables reveal crucial aspects for the definition of management strategies focused on promoting academic success.

Supervised
thesis

2019

Churn Prediction in Digital Marketing

Author
Inês de Carvalho Pereira Ferreira

Institution
UP-FEUP

2019

Learning to Classify a Subject-Line Quality for Email Marketing Using Data Mining Techniques

Author
Maria João dos Santos Aguiar e Mira Paulo

Institution
UP-FEUP

2019

Analysis of Usage Patterns of Medical Image Exams in Medical Environments

Author
Nuno Martins Marques Pinto

Institution
UP-FEUP

2019

Developing business analytics to support kick-scooters sharing systems operators

Author
João António Abreu Costa de Sousa

Institution
UP-FEUP

2019

Early adoption of trends in food-based retailing

Author
Francisco Manuel Gonçalves Barbosa

Institution
UP-FEUP