Página InicialGruposDiscussãoMaisZeitgeist
Pesquisar O Sítio Web
Este sítio web usa «cookies» para fornecer os seus serviços, para melhorar o desempenho, para analítica e (se não estiver autenticado) para publicidade. Ao usar o LibraryThing está a reconhecer que leu e compreende os nossos Termos de Serviço e Política de Privacidade. A sua utilização deste sítio e serviços está sujeita a essas políticas e termos.

Resultados dos Livros Google

Carregue numa fotografia para ir para os Livros Google.

A carregar...

Recommendation Engines (The MIT Press Essential Knowledge series)

por Michael Schrage

MembrosCríticasPopularidadeAvaliação médiaDiscussões
26Nenhum(a)889,335 (5)Nenhum(a)
"How does Netflix know just what to suggest you watch next? How does Amazon determine what a "customer like you" has also purchased? The answer is recommender systems, the technological concept that lies at the heart of most of the successful companies in the digital economy. Michael Schrage starts with the origins of recommender systems, which go back further than you think (see: the Oracle at Delphi for one of history's earliest recommenders), and a history of the first companies to harness recommendations. He then discusses the technology behind how recommenders work: the AI and machine learning algorithms that power these recommender platforms. Next he discusses the role of user experience, and how recommender systems are designed, and how design choices function as nudges to make certain recommendations more salient than others. He explores three case studies: Spotify, Bytedance, and Stitch Fix, looking at how recommenders can create new business solutions and how algorithms can go beyond curation to content creation. The concluding chapter on the future of recommender systems is perhaps the most enlightening. Moving away from technology and business, Schrage embraces the philosophical, probing the role of free will in a world mediated by recommender systems (a recommendation inherently offers a choice; without the element of choice, any digital manipulation of our preferences cannot truly be called a "recommendation"), and exploring the role of recommender systems as a means of improving the self. In the vein of Free Will, this book presents the essential information while revealing the author's point of view. Schrage wants to push our understanding of recommender systems beyond the technological, to understand what societal role they play and what opportunities they offer now and in the future"--… (mais)
Nenhum(a)
A carregar...

Adira ao LibraryThing para descobrir se irá gostar deste livro.

Ainda não há conversas na Discussão sobre este livro.

Sem comentários
sem críticas | adicionar uma crítica
Tem de autenticar-se para poder editar dados do Conhecimento Comum.
Para mais ajuda veja a página de ajuda do Conhecimento Comum.
Título canónico
Título original
Títulos alternativos
Data da publicação original
Pessoas/Personagens
Locais importantes
Acontecimentos importantes
Filmes relacionados
Epígrafe
Dedicatória
Primeiras palavras
Citações
Últimas palavras
Nota de desambiguação
Editores da Editora
Autores de citações elogiosas (normalmente na contracapa do livro)
Língua original
DDC/MDS canónico
LCC Canónico

Referências a esta obra em recursos externos.

Wikipédia em inglês

Nenhum(a)

"How does Netflix know just what to suggest you watch next? How does Amazon determine what a "customer like you" has also purchased? The answer is recommender systems, the technological concept that lies at the heart of most of the successful companies in the digital economy. Michael Schrage starts with the origins of recommender systems, which go back further than you think (see: the Oracle at Delphi for one of history's earliest recommenders), and a history of the first companies to harness recommendations. He then discusses the technology behind how recommenders work: the AI and machine learning algorithms that power these recommender platforms. Next he discusses the role of user experience, and how recommender systems are designed, and how design choices function as nudges to make certain recommendations more salient than others. He explores three case studies: Spotify, Bytedance, and Stitch Fix, looking at how recommenders can create new business solutions and how algorithms can go beyond curation to content creation. The concluding chapter on the future of recommender systems is perhaps the most enlightening. Moving away from technology and business, Schrage embraces the philosophical, probing the role of free will in a world mediated by recommender systems (a recommendation inherently offers a choice; without the element of choice, any digital manipulation of our preferences cannot truly be called a "recommendation"), and exploring the role of recommender systems as a means of improving the self. In the vein of Free Will, this book presents the essential information while revealing the author's point of view. Schrage wants to push our understanding of recommender systems beyond the technological, to understand what societal role they play and what opportunities they offer now and in the future"--

Não foram encontradas descrições de bibliotecas.

Descrição do livro
Resumo Haiku

Current Discussions

Nenhum(a)

Capas populares

Ligações Rápidas

Avaliação

Média: (5)
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5 1

É você?

Torne-se num Autor LibraryThing.

 

Acerca | Contacto | LibraryThing.com | Privacidade/Termos | Ajuda/Perguntas Frequentes | Blogue | Loja | APIs | TinyCat | Bibliotecas Legadas | Primeiros Críticos | Conhecimento Comum | 204,507,895 livros! | Barra de topo: Sempre visível