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The Knowledge Map Analysis of User Profile Research Based on CiteSpace

机译:基于CiteSpace的用户档案研究的知识图谱分析

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With the development of big data technology, user profile, as an effective method for delineating user characteristics, has attracted extensive attention from researchers and practitioners. Rich related literatures have been accumulated. How to find the key factors and the new direction from such a big library is a difficult problem for a new researcher entering the field. The knowledge map can be used to visualize the development trend, the frontier field and the overall knowledge structure from these researches. Therefore, we choose web of science database as the literature search engine and use CiteSpace to construct the user profile knowledge map. Through these maps, we analyze the important authors and countries, make the common word analysis and co-citation analysis, study the hot spots and important literatures. The time distribution shows that some foundational theories in user profile were produced at the second stage from 2004 to 2013. What's more, from the geographical distribution, we find that user profile, as an abstract concept, has no unified framework. Each country focuses on the different research points. From the knowledge map of keywords, we find that the top three algorithmic techniques used in constructing user profile are clustering, classification, and collaborative filtering. At the same time, user profile is also used in some specific applications, such as anomaly detection, behavior analysis, and information retrieval.
机译:随着大数据技术的发展,作为描述用户特征的一种有效方法,用户档案已引起研究人员和从业人员的广泛关注。积累了丰富的相关文献。如何从如此庞大的图书馆中找到关键因素和新方向是新研究人员进入该领域的难题。知识图谱可用于可视化这些研究的发展趋势,前沿领域和整体知识结构。因此,我们选择Web of Science数据库作为文献搜索引擎,并使用CiteSpace构建用户资料知识图。通过这些地图,我们分析了重要的作者和国家,进行了常用词分析和引文分析,研究了热点和重要文献。时间分布表明,用户配置文件的一些基础理论是在2004年至2013年的第二阶段产生的。此外,从地理分布来看,我们发现用户配置文件作为一个抽象概念没有统一的框架。每个国家侧重于不同的研究点。从关键字的知识图谱中,我们发现用于构建用户配置文件的前三种算法技术是聚类,分类和协作过滤。同时,用户配置文件还用于某些特定的应用程序中,例如异常检测,行为分析和信息检索。

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