AccessRank: predicting what users will do next
AccessRank: predicting what users will do next
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AccessRank：预测用户接下来会做什么
DOI：
10.1145/2207676.2208380
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
A. Cockburn
中科院分区：
文献类型：
--
作者：
Stephen Fitchett;A. Cockburn
我们介绍了一种算法，预测在许多情况下，如文件访问，网站访问，窗口开关，和命令行的revisitations和重用。Rank使用许多输入源来生成其预测，包括新近度、频率、时间聚类和时间。基于各种应用程序中真实的用户交互的日志记录的模拟表明，与其他算法相比，Rank更准确地预测即将到来的访问。由Rank生成的预测列表也比其他具有良好预测能力的算法更稳定，当项目以列表形式呈现时，这对于可用性非常重要，因为用户可以依靠他们的空间记忆来确定目标位置。最后，我们给出了真实的应用程序如何使用Rank的示例。
We introduce AccessRank, an algorithm that predicts revisitations and reuse in many contexts, such as file accesses, website visits, window switches, and command lines. AccessRank uses many sources of input to generate its predictions, including recency, frequency, temporal clustering, and time of day. Simulations based on log records of real user interaction across a diverse range of applications show that AccessRank more accurately predicts upcoming accesses than other algorithms. The prediction lists generated by AccessRank are also shown to be more stable than other algorithms that have good predictive capability, which can be important for usability when items are presented in lists as users can rely on their spatial memory for target location. Finally, we present examples of how real world applications might use AccessRank.