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Email recipient prediction using reverse chronologically arranged implicit groups

机译：使用反向按时间顺序排列的隐式组的电子邮件收件人预测

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Although social networking has significantly influenced online communication, email still has managed to retain its importance. There are number of techniques proposed in past by researchers for recipient prediction/suggestion. Most of them are complex to implement and takes good amount of computation time. The major factor behind higher time complexity and space complexity is the prediction models these methods use. These days mobile device applications are being widely used for emailing and thus appropriate techniques should be found considering constraints of mobile devices. Keeping this in view our research focuses on proposing prediction model, which takes very less computational efforts to be maintained. Apart from this, existing methods focus on maximizing number of intended recipients in one prediction cycle. In this paper, we also propose a different way of looking at the problem, by targeting 1 intended recipient in each iteration. For this, we introduce hit rate as a good measurement technique to measure the effectiveness of recipient prediction algorithm. We also present a flaw in the compiled version of Enron data set, and show some novel analysis on Enron data set which will help immensely in creating efficient recipient prediction algorithm.
机译：尽管社交网络极大地影响了在线交流，但电子邮件仍然设法保持了其重要性。研究人员过去提出了许多用于收件人预测/建议的技术。它们中的大多数实现起来都很复杂，并且需要大量的计算时间。时间复杂度和空间复杂度较高的主要原因是这些方法使用的预测模型。如今，移动设备应用程序已广泛用于电子邮件发送，因此，考虑到移动设备的限制，应找到适当的技术。考虑到这一点，我们的研究集中在提出预测模型上，该模型需要维护的计算量很少。除此之外，现有方法集中于在一个预测周期中最大化预期接收者的数量。在本文中，我们还提出了另一种解决问题的方法，即在每次迭代中针对1个预期的接收者。为此，我们将命中率作为一种很好的测量技术来衡量收件人预测算法的有效性。我们还介绍了Enron数据集的编译版本中的缺陷，并显示了对Enron数据集的一些新颖分析，这将极大地帮助创建有效的收件人预测算法。

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