KondoCloud: Improving Information Management in Cloud Storage via Recommendations Based on File Similarity
KondoCloud: Improving Information Management in Cloud Storage via Recommendations Based on File Similarity
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DOI：
10.1145/3472749.3474736
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Will Brackenbury;A. Mcnutt;K. Chard;Aaron J. Elmore;Blase Ur
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作者：
Will Brackenbury;A. Mcnutt;K. Chard;Aaron J. Elmore;Blase Ur
用户在保持个人文件收藏井井有条方面面临着许多挑战。虽然当前的文件管理界面可以帮助用户检索杂乱的存储库中的文件，但它们无助于组织。相关文件可能很难找到，并且本应删除的文件可能仍然存在。为了提供帮助，我们设计了 KondoCloud，这是一个用于个人云存储的文件浏览器界面。 KondoCloud 基于机器学习推荐用户可能想要检索、移动或删除的文件。这些建议利用了类似文件应该以类似方式进行管理的直觉。我们通过两项互补的在线用户研究开发和评估了 KondoCloud。在我们的观察研究中，我们记录了 69 名参与者的行为，他们花了 30 分钟手动组织自己的 Google Drive 存储库。我们确定了高级组织策略，包括将相关文件移动到新创建的子文件夹和广泛删除文件。为了训练支持 KondoCloud 建议的分类器，我们让参与者标记文件对是否相似以及是否应该以类似方式管理它们。此外，我们从参与者存储库中的所有文件中提取了十个元数据和内容特征。我们的逻辑回归分类器的 F1 分数均达到 0.72 或更高。在我们的评估研究中，62 名参与者在有或没有推荐的情况下使用了 KondoCloud。大约一半的参与者接受了一部分建议，有些参与者几乎接受了全部建议。看到建议的参与者更有可能删除位于不同目录中的相关文件。他们还普遍认为这些建议提高了效率。尽管如此，没有看到建议的参与者仍然手动执行了约三分之一的建议操作。
Users face many challenges in keeping their personal file collections organized. While current file-management interfaces help users retrieve files in disorganized repositories, they do not aid in organization. Pertinent files can be difficult to find, and files that should have been deleted may remain. To help, we designed KondoCloud, a file-browser interface for personal cloud storage. KondoCloud makes machine learning-based recommendations of files users may want to retrieve, move, or delete. These recommendations leverage the intuition that similar files should be managed similarly. We developed and evaluated KondoCloud through two complementary online user studies. In our Observation Study, we logged the actions of 69 participants who spent 30 minutes manually organizing their own Google Drive repositories. We identified high-level organizational strategies, including moving related files to newly created sub-folders and extensively deleting files. To train the classifiers that underpin KondoCloud’s recommendations, we had participants label whether pairs of files were similar and whether they should be managed similarly. In addition, we extracted ten metadata and content features from all files in participants’ repositories. Our logistic regression classifiers all achieved F1 scores of 0.72 or higher. In our Evaluation Study, 62 participants used KondoCloud either with or without recommendations. Roughly half of participants accepted a non-trivial fraction of recommendations, and some participants accepted nearly all of them. Participants who were shown the recommendations were more likely to delete related files located in different directories. They also generally felt the recommendations improved efficiency. Participants who were not shown recommendations nonetheless manually performed about a third of the actions that would have been recommended.