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A System for Email Recipient Prediction

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Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

The ability to accurately predict recipients of an email, while it is being composed, is of great practical importance for two reasons. First, prediction of recipients allows for effective “auto-complete” of this field, thereby improving user experience and reducing the overhead of manual typing of the recipient. Second, this capability allows the system to alert the user when she has typed unlikely recipients. Such alerts can help avoid human error that might result in forgetting relevant recipients, or, even worse, disclosure of personal or classified information.In this article, a system that effectively predicts email recipients, given an email history, will be presented. The system takes into consideration a variety of email related features to achieve high accuracy. Extensive experimentation on diverse email corpora has shown that our system adapts well to a variety of domains (such as business, personal and political email).

Keywords

  • Recipient prediction problem
  • Greeting feature
  • Enron dataset
  • Cross-user approach
  • Feature selection problem

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  • DOI: 10.1007/978-3-319-51367-6_2
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Notes

  1. 1.

    http://lucene.apache.org/

  2. 2.

    Some emails have several recipients and more than one name in the greeting. In such cases, we cannot distinguish to which recipient each name refers, and therefore \(\mathcal{G}_{m}\), for an email sent to c, may actually contain a name not referring to c.

  3. 3.

    In one case “more recent incoming percentage” outperformed the personalized function. This seems to be due to the fact that the Gmail datasets were mostly small accounts, except for two very large accounts of users—including one of the authors of this article—who have a compulsive habit of immediately answering every email they receive. For larger and more diverse datasets, we expect the personalized function to be the best performing.

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Acknowledgements

Zvi Sofershtein and Sara Cohen were partially supported by the Israel Science Foundation (Grant 1467/13) and the Ministry of Science and Technology (Grant 3-9617).

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Correspondence to Sara Cohen .

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Sofershtein, Z., Cohen, S. (2017). A System for Email Recipient Prediction. In: Kaya, M., Erdoǧan, Ö., Rokne, J. (eds) From Social Data Mining and Analysis to Prediction and Community Detection. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-51367-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-51367-6_2

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