BIGFile: Bayesian Information Gain for Fast File Retrieval
BIGFile: Bayesian Information Gain for Fast File Retrieval
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BIGFile：用于快速文件检索的贝叶斯信息增益
DOI：
10.1145/3173574.3173959
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Beaudouin
中科院分区：
文献类型：
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作者：
Wanyu Liu;O. Rioul;Joanna McGrenere;W. Mackay;M. Beaudouin
我们介绍了BIGFile，一个新的快速文件检索技术的基础上贝叶斯信息增益框架。BIGFile提供界面快捷方式来帮助用户导航到所需的目标（文件或文件夹）。BIGFile的拆分界面将传统的列表视图与自适应区域相结合，该自适应区域显示由我们的计算效率算法估计的文件路径集的快捷方式。用户可以像往常一样导航列表，或选择自适应区域中的路径的任何部分。对15名用户的试点研究为BIGFile的设计提供了信息，揭示了他们的文件系统的大小和结构以及他们的文件检索实践。我们的模拟表明，BIGFile优于Fitchett等人。的最佳预测算法，我们进行了一个实验来比较BIGFile与ARFile（在拆分接口中实例化的Rank），并以类似Finder的列表视图作为基线。BIGFile是迄今为止最有效的技术（比ARFile快44%，比RFile快64%），参与者一致喜欢拆分界面而不是RFile。
We introduce BIGFile, a new fast file retrieval technique based on the Bayesian Information Gain framework. BIGFile provides interface shortcuts to assist the user in navigating to a desired target (file or folder). BIGFile's split interface combines a traditional list view with an adaptive area that displays shortcuts to the set of file paths estimated by our computationally efficient algorithm. Users can navigate the list as usual, or select any part of the paths in the adaptive area. A pilot study of 15 users informed the design of BIGFile, revealing the size and structure of their file systems and their file retrieval practices. Our simulations show that BIGFile outperforms Fitchett et al.'s AccessRank, a best-of-breed prediction algorithm. We conducted an experiment to compare BIGFile with ARFile (AccessRank instantiated in a split interface) and with a Finder-like list view as baseline. BIGFile was by far the most efficient technique (up to 44% faster than ARFile and 64% faster than Finder), and participants unanimously preferred the split interfaces to the Finder.