淡江大學機構典藏:Item 987654321/120938
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    题名: Investigating online social media users' behaviors for social commerce recommendations
    作者: Liao, S.H.;Widowati, R.;Hsieh Y.C.
    关键词: Online social media;Social commerce;Electronic commerce;Data mining;Knowledge discovery;Recommendation systems
    日期: 2021-10-30
    上传时间: 2021-08-18 12:14:17 (UTC+8)
    摘要: Online social media create virtual communities and network platforms that people use to create, share, and exchange opinions, views and experiences. With social networks, social commerce not only relies on commerce, but online social media can also promote the sale of goods or services online. Many online operators have begun to use recommendation systems to analyze customer purchase history and identify individual products that customers may purchase. This enables the company to send product information to consumers to attract their attention. In addition, consumers have a higher purchase rate for recommended products based on consumer data. Based on a survey in Taiwan society, this study uses the questionnaire survey method to collect data on a relational database. This study investigates Taiwan online social media users’ behaviors using data mining methods, including clustering analysis and association rules. Clustering analysis is to investigate possible profiles of users and association rules are to find knowledge patterns and rules of user profiles, online social media usage motivation/preferences and social commerce behavior in order to generate social commerce recommendations in terms of social technology development in the modern society.
    關聯: Technology in Society 66, 101655
    DOI: 10.1016/j.techsoc.2021.101655
    显示于类别:[管理科學學系暨研究所] 期刊論文

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