A Framework for Recommendation of Highly Popular News Lacking Social Feedback
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/5189 http://dx.doi.org/10.1007/s00354-017-0019-x |
Resumo: | 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 |
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A Framework for Recommendation of Highly Popular News Lacking Social FeedbackSocial 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 Japan2017-12-31T16:33:26Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5189http://dx.doi.org/10.1007/s00354-017-0019-xengNuno Miguel MonizLuís TorgoEirinaki,MPaula Oliveira Brancoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:13Zoai:repositorio.inesctec.pt:123456789/5189Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:50.155309Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A Framework for Recommendation of Highly Popular News Lacking Social Feedback |
title |
A Framework for Recommendation of Highly Popular News Lacking Social Feedback |
spellingShingle |
A Framework for Recommendation of Highly Popular News Lacking Social Feedback Nuno Miguel Moniz |
title_short |
A Framework for Recommendation of Highly Popular News Lacking Social Feedback |
title_full |
A Framework for Recommendation of Highly Popular News Lacking Social Feedback |
title_fullStr |
A Framework for Recommendation of Highly Popular News Lacking Social Feedback |
title_full_unstemmed |
A Framework for Recommendation of Highly Popular News Lacking Social Feedback |
title_sort |
A Framework for Recommendation of Highly Popular News Lacking Social Feedback |
author |
Nuno Miguel Moniz |
author_facet |
Nuno Miguel Moniz Luís Torgo Eirinaki,M Paula Oliveira Branco |
author_role |
author |
author2 |
Luís Torgo Eirinaki,M Paula Oliveira Branco |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Nuno Miguel Moniz Luís Torgo Eirinaki,M Paula Oliveira Branco |
description |
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 |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-12-31T16:33:26Z 2017-01-01T00:00:00Z 2017 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.inesctec.pt/handle/123456789/5189 http://dx.doi.org/10.1007/s00354-017-0019-x |
url |
http://repositorio.inesctec.pt/handle/123456789/5189 http://dx.doi.org/10.1007/s00354-017-0019-x |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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