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Titlebook: Online Social Media Content Delivery; A Data-Driven Approa Zhi Wang,Wenwu Zhu,Shiqiang Yang Book 2018 The Author(s) 2018 Content Delivery N

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发表于 2025-3-21 18:23:08 | 显示全部楼层 |阅读模式
书目名称Online Social Media Content Delivery
副标题A Data-Driven Approa
编辑Zhi Wang,Wenwu Zhu,Shiqiang Yang
视频video
概述Introduces a data-driven approach to design strategies for social media content delivery.Explains how data-driven models can be used in the context of social media content propagation and popularity.P
丛书名称SpringerBriefs in Computer Science
图书封面Titlebook: Online Social Media Content Delivery; A Data-Driven Approa Zhi Wang,Wenwu Zhu,Shiqiang Yang Book 2018 The Author(s) 2018 Content Delivery N
描述This book explains how to use a data-driven approach to design strategies for social media content delivery. It first introduces readers to how social information can be effectively gathered for big data analysis, which provides content delivery intelligence. Secondly, the book describes data-driven models to capture information diffusion in online social networks and social media content propagation and popularity, before presenting prediction models for social media content delivery. By addressing the resource allocation and content replication aspects of social media content delivery, the book presents the latest data-driven strategies. In closing, it outlines a number of potential research directions regarding social media content delivery..
出版日期Book 2018
关键词Content Delivery Network; Big Data; Edge Network; Social Media; Online Social Network; Social Media Conte
版次1
doihttps://doi.org/10.1007/978-981-10-2774-1
isbn_softcover978-981-10-2773-4
isbn_ebook978-981-10-2774-1Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s) 2018
The information of publication is updating

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发表于 2025-3-21 21:33:43 | 显示全部楼层
Book 2018 information can be effectively gathered for big data analysis, which provides content delivery intelligence. Secondly, the book describes data-driven models to capture information diffusion in online social networks and social media content propagation and popularity, before presenting prediction m
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Joint Online Processing and Geo-Distributed Delivery for Dynamic Social Streaming,uch customization usually requires computation resources in the content delivery flow. This chapter presents some exploration into incorporating content processing in the social content delivery framework.
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ity of user-user matrix and user-item matrix issue create severe challenges, causing collaborative filtering methods to degrade significantly in their recommendation performance. Moreover, the factors those affect users’ preference for items and friends are complex in social networks. For example, u
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