Artboard 33atençãoArtboard 18atualizarconectividadeArtboard 42boletocarrinhocartãoArtboard 45cartão SaraivacelularArtboard 42Artboard 23checkArtboard 28Artboard 17?compararcompartilharcompartilhar ativoArtboard 28Artboard 43Artboard 49Artboard 47Artboard 15Artboard 32ebookArtboard 22Artboard 5Artboard 25Artboard 1Artboard 42Artboard 11fecharfilmesArtboard 23gamesArtboard 4Artboard 9Artboard 6hqimportadosinformáticaArtboard 7Artboard 3Artboard 12Artboard 25Artboard 34Artboard 43Artboard 44curtirArtboard 24Artboard 13livrosArtboard 24Artboard 31menumúsicaArtboard 27Artboard 30Artboard 36Artboard 44outrospapelariaArtboard 17Artboard 6Artboard 27Artboard 30Artboard 29Artboard 26Artboard 2Artboard 20Artboard 35estrelaestrela ativorelógiobuscaArtboard 50Artboard 26toda saraivaArtboard 40Artboard 21Artboard 10Artboard 37usuárioArtboard 46Artboard 33Artboard 8seta
e-book

Learning from Multiple Social Networks (Cód: 9671335)

Chua,Tat-Seng; Song,Xuemeng; Nie,Liqiang

Morgan and Claypool Publishers (Livros Digitais)

Ooops! Este produto não está mais a venda.
Mas não se preocupe, temos uma versão atualizada para você.

Ooopss! Este produto está fora de linha, mas temos outras opções para você.
Veja nossas sugestões abaixo!

R$ 108,00

em até 3x de R$ 36,00 sem juros

Total:

Em até 1x sem juros de


Crédito:
Boleto:
Cartão Saraiva:

Total:

Em até 3x sem juros de


Learning from Multiple Social Networks

R$108,00

Descrição

With the proliferation of social network services, more and more social users, such as individuals and organizations, are simultaneously involved in multiple social networks for various purposes. In fact, multiple social networks characterize the same social users from different perspectives, and their contexts are usually consistent or complementary rather than independent. Hence, as compared to using information from a single social network, appropriate aggregation of multiple social networks offers us a better way to comprehensively understand the given social users.

Learning across multiple social networks brings opportunities to new services and applications as well as new insights on user online behaviors, yet it raises tough challenges: (1) How can we map different social network accounts to the same social users? (2) How can we complete the item-wise and block-wise missing data? (3) How can we leverage the relatedness among sources to strengthen the learning performance? And (4) How can we jointly model the dual-heterogeneities: multiple tasks exist for the given application and each task has various features from multiple sources? These questions have been largely unexplored to date.

We noticed this timely opportunity, and in this book we present some state-of-the-art theories and novel practical applications on aggregation of multiple social networks. In particular, we first introduce multi-source dataset construction. We then introduce how to effectively and efficiently complete the item-wise and block-wise missing data, which are caused by the inactive social users in some social networks. We next detail the proposed multi-source mono-task learning model and its application in volunteerism tendency prediction. As a counterpart, we also present a mono-source multi-task learning model and apply it to user interest inference. We seamlessly unify these models with the so-called multi-source multi-task learning, and demonstrate several application scenarios, such as occupation prediction. Finally, we conclude the book and figure out the future research directions in multiple social network learning, including the privacy issues and source complementarity modeling.

This is preliminary research on learning from multiple social networks, and we hope it can inspire more active researchers to work on this exciting area. If we have seen further it is by standing on the shoulders of giants.

Características

Produto sob encomenda Não
Marca Morgan and Claypool Publishers (Livros Digitais)
Cód. Barras 9781627059862
Acabamento e-book
Idioma 337
Número de Páginas 118 (aproximado)
Ano da Publicação 116
Peso 0.00 Kg
AutorChua,Tat-Seng; Song,Xuemeng; Nie,Liqiang