Review of Social Recommendation with Bibliometrics and Social Network Analysis
Li Fei, Zhang Jian(), Wang Zongshui
School of Economics&Management, Beijing Information Science&Technology University, Beijing 100192, China Beijing Key Lab of Green Development Decision Based on Big Data, Beijing 100192, China
[Objective] This paper summarizes the content characteristics and network evolution of social recommendation research based on the of bibliometrics and social network analysis. [Methods] First, we collected the data of social recommendation research from the Web of Science database. Then we analyzed the data with manual interpretation, keywords co-occurrence analysis, bibliometrics, social network analysis and data visualization. [Results] A total of 3701 articles on social recommendation were retrieved, which have been increasing recently. Based on the threshold of papers published each year, we divided the development of social recommendation research into three distinct stages. [Limitations] We only used keywords to explore the characteristics of the relevant document contents, which could be improved with in-depth text mining. There is lack of uniform criterion to classify the evolution stages of the related research. Our study only shows the changing of contents and development trends. [Conclusions] The international impacts of Chinese scholars have been rising in social recommendation studies, which highly focus on the topics of social media and collaborative filtering.
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