The Cloud Computing of Social Bookmarking System Based on PageRank and Reputation Model

碩士 === 國立交通大學 === 資訊管理研究所 === 100 === The development of Internet is thriving in recent years. People don't confined learn the new knowledge to books. After search engines appear, people can easily learn and search the new knowledge. Unfortunately amount of data is getting huge in the internet,...

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Main Authors: Shih, Po-Hong, 施柏宏
Other Authors: 楊千
Format: Others
Language:zh-TW
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/75375564940627910464
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spelling ndltd-TW-100NCTU53960172016-03-28T04:20:36Z http://ndltd.ncl.edu.tw/handle/75375564940627910464 The Cloud Computing of Social Bookmarking System Based on PageRank and Reputation Model 基於PageRank與聲譽模型之社群書籤雲端平台系統 Shih, Po-Hong 施柏宏 碩士 國立交通大學 資訊管理研究所 100 The development of Internet is thriving in recent years. People don't confined learn the new knowledge to books. After search engines appear, people can easily learn and search the new knowledge. Unfortunately amount of data is getting huge in the internet, many search engines do not explicitly support collaboration during search and this let people can not collaborate to filter data. This paper introduces two systems that social bookmarking website based on cloud computing and IE Toolbar. These systems support users by harnessing the bookmark's metadata、users' similarity calculation、PageRank and Reputation Model as the base for websites and users recommendation. Therefore, a key contribution of this paper is to develop a collaboration system and to design IE Toolbar to work with mainstream search engines. Let user can easily make friend with another user, collaborate to filter data and share their knowledge. Finally, the experiment invites 52 users and they use this system about one week. The research findings show that reputation, PageRank and citation times are positive relationship. Personality and interactivity can enrich the content and enlarge the user interesting in this system. 楊千 2012 學位論文 ; thesis 54 zh-TW
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language zh-TW
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description 碩士 === 國立交通大學 === 資訊管理研究所 === 100 === The development of Internet is thriving in recent years. People don't confined learn the new knowledge to books. After search engines appear, people can easily learn and search the new knowledge. Unfortunately amount of data is getting huge in the internet, many search engines do not explicitly support collaboration during search and this let people can not collaborate to filter data. This paper introduces two systems that social bookmarking website based on cloud computing and IE Toolbar. These systems support users by harnessing the bookmark's metadata、users' similarity calculation、PageRank and Reputation Model as the base for websites and users recommendation. Therefore, a key contribution of this paper is to develop a collaboration system and to design IE Toolbar to work with mainstream search engines. Let user can easily make friend with another user, collaborate to filter data and share their knowledge. Finally, the experiment invites 52 users and they use this system about one week. The research findings show that reputation, PageRank and citation times are positive relationship. Personality and interactivity can enrich the content and enlarge the user interesting in this system.
author2 楊千
author_facet 楊千
Shih, Po-Hong
施柏宏
author Shih, Po-Hong
施柏宏
spellingShingle Shih, Po-Hong
施柏宏
The Cloud Computing of Social Bookmarking System Based on PageRank and Reputation Model
author_sort Shih, Po-Hong
title The Cloud Computing of Social Bookmarking System Based on PageRank and Reputation Model
title_short The Cloud Computing of Social Bookmarking System Based on PageRank and Reputation Model
title_full The Cloud Computing of Social Bookmarking System Based on PageRank and Reputation Model
title_fullStr The Cloud Computing of Social Bookmarking System Based on PageRank and Reputation Model
title_full_unstemmed The Cloud Computing of Social Bookmarking System Based on PageRank and Reputation Model
title_sort cloud computing of social bookmarking system based on pagerank and reputation model
publishDate 2012
url http://ndltd.ncl.edu.tw/handle/75375564940627910464
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