A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News
By Kelwin Fernandes, Pedro Vinagre, Paulo Cortez. 2015
231 citations recorded by the source corpus.
Subfields: Information Systems, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Eric P. Xing
- Rosie Yuyan Zou
- Barnab'as P'oczos
- Jeff Schneider
- Matthias Schonlau
- Kirthevasan Kandasamy
- Ladislav Rampášek
- Chun-Hao Kingsley Chang
In Statistical and Nonlinear Physics
- Anna Goldenberg
- Eric P. Xing
- Rosie Yuyan Zou
- Barnab'as P'oczos
- Jeff Schneider
- Matthias Schonlau
- Kirthevasan Kandasamy
- Ladislav Rampášek
Citations in the corpus
- Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018, methodology): “Datasets: We use the following datasets: blog feedback [4], indoor location [43], slice localisation [11], naval propulsion [5], protein tertiary structure [31], news popularity [7], Cifar10 [21].”
- The random forest algorithm for statistical learning (2020, methodology): “16 The random forest algorithm for statistical learning 5 Example: Online news popularity Fernandes et al. (2015) and Dheeru and Karra Taniskidou (2017) investigated the popularity of online news.”
- CAUSAL INTERPRETATIONS OF BLACK-BOX MODELS (2019, unknown): “Fernandes, Vinagre, and Cortez (2015) gathered 39, 797 news article published by Mashable and used 58 predictor variables to predict the number of shares in social networks.”
- PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers (2016, background): “…3) company Insurance study (of dimension: 9 , 882 × 38 ) for predicting caravan issurance from demographic information and personal finance attributes, and 4) News dataset (of dimension 39 , 082 × 52 ) [Fernandes et al., 2015] for predicting the popularity of Mashable.com news from article features.”
- Comparison-Based Inverse Classification for Interpretability in Machine Learning (2018, methodology): “Experimental Protocol The news popularity dataset [8] is made of 39644 online articles from website Mashable, described by 58 numerical features.”
- Dropout Feature Ranking for Deep Learning Models (2017, methodology): “ network architecture. In this experiment, we evaluate our ranking approaches on 2 classification tasks (clinical dataset Support21, UCI MiniBooNE datasets),and2UCIregressiontasks(OnlineNewsPopularity(Fernandes et al., 2015), YearPredictionMSD). Here we describe each dataset. Support2 is a multivaria”