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Beyond Text Querying and Ranking List: How People are Searching through Faceted Catalogs in Two Library Environments

机译:除了文本查询和排名列表之外:人们如何通过两个库环境中的面位目录搜索

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This paper reports the result of a transaction log analysis on two faceted library catalogs (University of North Carolina at Chapel Hill (UNC) Library catalog and Phoenix Public Library (PPL) catalog). The goal is to investigate people’s searching behavior with the faceted catalogs in an academic library and a public library. Two large data sets (with 504,142 logs for 40 days, and 1,010,239 logs for 60 days respectively) are analyzed. Descriptive statistics are reported and cluster analysis is conducted. It is found that people do incorporate facets when they are searching through a faceted catalog for either the academic or the public library. The facet usage for the PPL catalog is higher than that of its UNC counterpart probably due to its support of facet browsing in addition to facet refining, and that they have facets that describe the content better. For the UNC data, many popular facets are the “administrative” (or circulative) metadata rather than the “real content” metadata, whereas for the PPL data, frequently used facets are the browsing titles appearing as search tabs on the search page, such as books and movies. Finally, cluster analysis reveals common search groups across the two library environments. A better understanding of people’s searching behavior can help system developers to develop more responsive systems to cater to different behaviors and different patrons. Particularly, an insight into how people are using facets will guide library technical staff to make facets more effective in helping people find what they want.
机译:本文报告了交易日志分析的结果,对两个面位的图书馆目录(Chapel Hill(UNC)图书馆目录和凤凰公共图书馆(PPL)目录的北卡罗来纳大学)。目标是在学术图书馆和公共图书馆中调查人们的搜索行为。分析了两个大型数据集(分别为504,142日志,分别为60天,分别为1,010,239日志)。报告描述性统计数据并进行集群分析。有人发现,当他们在学术或公共图书馆搜索各个目录时,人们会纳入方面。 PPL目录的小方面使用率高于其UNC对应于其UNC对应物的使用,这可能是由于外部浏览的支持外,并且它们具有更好地描述内容的方面。对于UNC数据,许多流行的面部是“管理”(或循环)元数据而不是“真实内容”元数据,而对于PPL数据,通常使用的方面是在搜索页面上显示为搜索选项卡的浏览标题,例如作为书籍和电影。最后,群集分析在两个库环境中显示了常见的搜索组。更好地了解人们的搜索行为可以帮助系统开发人员开发更多敏感系统,以满足不同的行为和不同的顾客。特别是,对人们如何使用FacTet的了解,将引导图书馆技术人员,使方面更有效地帮助人们找到他们想要的东西。

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