A Framework for Recommendation of Highly Popular News Lacking Social Feedback

• Tipo: Artigo em Revista Científica Internacional
• Data: 2017

Autores

Moniz, N (Autor) Outra; Torgo, L (Autor) FCUP; Eirinaki, M (Autor) Outra; Branco, P (Autor) Outra

Revista

Título: New Generation Computing

Vol. 35

Páginas: 417-450

ISSN: 0288-3635

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-00M-VY0

DOI: 10.1007/s00354-017-0019-x

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 34