社会化推荐研究进展与发展趋势演化——基于文献计量和社会网络分析的视角

Review of Social Recommendation with Bibliometrics and Social Network Analysis

【目的】从文献计量和社会网络分析的角度对社会化推荐进行内容特征及网络演化的研究,归纳领域研究热点和发展趋势。【方法】以Wo S数据库为数据源,采用人工判读法、关键词共现、文献计量、社会网络分析及数据可视化等方法对样本数据进行数据挖掘和关联分析。【结果】检索到社会化推荐类文献3 701篇,论文数量整体呈上升趋势,以发文量阈值为阶段划分标准,将社会化推荐的发展演化趋势划分为三个阶段,各阶段研究特征明显。【局限】仅以关键词为探究文献内容特征的依据,内容深度挖掘相对不足,其中阶段划分是为了分析研究内容及演化趋势的变化,并不存在统一的划分标准。【结论】我国学者在该领域的国际影响力逐年上升。

[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.