Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud Storage
Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud Storage
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DOI：
10.1145/3526113.3545704
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Will Brackenbury;K. Chard;Aaron J. Elmore;Blase Ur
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文献类型：
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作者：
Will Brackenbury;K. Chard;Aaron J. Elmore;Blase Ur
个人云存储系统越来越多地提供建议，帮助用户检索或管理感兴趣的文件。例如，谷歌驱动器的快速访问预测并显示可能被访问的文件。然而，当提出多个相关的建议时，界面通常单独显示推荐文件和任何附带的解释，这给用户带来了负担。为了提高机器学习驱动的个人信息管理系统的可用性，我们提出了一种新的方法来总结相关的文件管理建议。我们生成被推荐的相关文件组的简洁摘要。摘要引用文件的共享特征。通过一项主题内在线研究，参与者在他们自己的b谷歌驱动器中收到对文件组的建议，我们将我们的总结与基线进行比较，如可视化决策树模型或简单列出组中的文件。与基线相比，当看到我们新颖的推荐摘要时，参与者对接受推荐表现出更大的理解和信心。
Personal cloud storage systems increasingly offer recommendations to help users retrieve or manage files of interest. For example, Google Drive’s Quick Access predicts and surfaces files likely to be accessed. However, when multiple, related recommendations are made, interfaces typically present recommended files and any accompanying explanations individually, burdening users. To improve the usability of ML-driven personal information management systems, we propose a new method for summarizing related file-management recommendations. We generate succinct summaries of groups of related files being recommended. Summaries reference the files’ shared characteristics. Through a within-subjects online study in which participants received recommendations for groups of files in their own Google Drive, we compare our summaries to baselines like visualizing a decision tree model or simply listing the files in a group. Compared to the baselines, participants expressed greater understanding and confidence in accepting recommendations when shown our novel recommendation summaries.