Resampling Approaches to Improve News Importance Prediction

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Nuno Moniz (Author) Other; Luis Torgo (Author) FCUP; Fatima Rodrigues (Author) Other

Conference proceedings International

Title: ADVANCES IN INTELLIGENT DATA ANALYSIS XIII

Pages: 215-226

13th International Symposium on Intelligent Data Analysis (IDA)

Leuven, BELGIUM, OCT 30-NOV 01, 2014

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 4 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-009-YX1

DOI: 10.1007/978-3-319-12571-8_19

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12