Please use this identifier to cite or link to this item:
Title: A Framework for Recommendation of Highly Popular News Lacking Social Feedback
Authors: Nuno Miguel Moniz
Luís Torgo
Paula Oliveira Branco
Issue Date: 2017
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 Ohmsha, Ltd. and Springer Japan
metadata.dc.type: article
Appears in Collections:LIAAD - Articles in International Journals

Files in This Item:
File Description SizeFormat 
P-00M-VY0.pdf2.13 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.