Big data analytics of social network marketing and personalized recommendations

doi:10.1007/s13278-021-00729-z

淡江大學機構典藏 > 商管學院 > 管理科學學系暨研究所 > 期刊論文 > Item 987654321/119868

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题名:	Big data analytics of social network marketing and personalized recommendations
作者:	Shu-Hsien Liao;Ching-An Yang
关键词:	Fans;Fan page;Social network marketing;Big data analytics;Personalized recommendations
日期:	2021-02-13
上传时间:	2021-01-23 12:10:19 (UTC+8)
出版者:	Springer Wien
摘要:	A fan page is a kind of a social network. Social network marketing (SNM) is a form of Internet marketing involving the creation and sharing of content on social media networks to achieve marketing and selling goals. In addition, precise SNM requires sufficient data and analysis in terms of making accurate online recommendations. This study examines the experience of various Taiwanese fan page users utilizing a market survey, a total of 1032 valid questionnaire data, and the questionnaire is divided into five sections with 33 items in terms of a big data structure based on a relational database on the first research stage. All questions use nominal and ordinal scales. In the second stage, this study develops a personalized recommendation system (PRS) using big data analytics approach, including cluster analysis and association rules. This study shows how the research results can obtain fans behavior knowledge by examining different group profiles and develop rule-based recommendation approach to generate personalized recommendations for building a SNM mechanism.
關聯:	Social Network Analysis and Mining 11
DOI:	10.1007/s13278-021-00729-z
显示于类别:	[管理科學學系暨研究所] 期刊論文

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