Email reply prediction: a machine learning approach

Taiwo Ayodele; Shikun Zhou; Rinat Khusainov

doi:10.1007/978-3-642-02559-4_13

Email reply prediction: a machine learning approach

Taiwo Ayodele, Shikun Zhou, Rinat Khusainov

Research output: Chapter in Book/Report/Published conference proceeding › Conference contribution › peer-review

Abstract

Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize and prioritize their email messages, we propose a new framework; email reply prediction with unsupervised learning. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.

Original language	English
Title of host publication	Human Interface and the Management of Information. Information and Interaction
Subtitle of host publication	Symposium on Human Interface 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009, Proceedings, Part II
Editors	Gavriel Salvendy, Michael J. Smith
Publisher	Springer Berlin
Pages	114-123
Number of pages	10
ISBN (Electronic)	978-3-642-02559-4
ISBN (Print)	978-3-642-02558-7
DOIs	https://doi.org/10.1007/978-3-642-02559-4_13
Publication status	Published - 19 Jul 2009
Externally published	Yes
Event	Symposium on Human Interface 2009 - San Diego, United States Duration: 19 Jul 2009 → 24 Jul 2009 https://www.hci.international/index.php?module=conference&CF_op=view&CF_id=21

Conference

Conference	Symposium on Human Interface 2009
Country/Territory	United States
City	San Diego
Period	19/07/09 → 24/07/09
Internet address	https://www.hci.international/index.php?module=conference&CF_op=view&CF_id=21

Access to Document

10.1007/978-3-642-02559-4_13

Cite this

Ayodele, T., Zhou, S., & Khusainov, R. (2009). Email reply prediction: a machine learning approach. In G. Salvendy, & M. J. Smith (Eds.), Human Interface and the Management of Information. Information and Interaction: Symposium on Human Interface 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009, Proceedings, Part II (pp. 114-123). Springer Berlin. https://doi.org/10.1007/978-3-642-02559-4_13

Ayodele, Taiwo ; Zhou, Shikun ; Khusainov, Rinat. / Email reply prediction: a machine learning approach. Human Interface and the Management of Information. Information and Interaction: Symposium on Human Interface 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009, Proceedings, Part II. editor / Gavriel Salvendy ; Michael J. Smith. Springer Berlin, 2009. pp. 114-123

@inproceedings{f076c24510844c70aee024a08688dba3,

title = "Email reply prediction: a machine learning approach",

abstract = "Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize and prioritize their email messages, we propose a new framework; email reply prediction with unsupervised learning. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.",

author = "Taiwo Ayodele and Shikun Zhou and Rinat Khusainov",

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booktitle = "Human Interface and the Management of Information. Information and Interaction",

publisher = "Springer Berlin",

address = "Germany",

note = "Symposium on Human Interface 2009 ; Conference date: 19-07-2009 Through 24-07-2009",

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Ayodele, T, Zhou, S & Khusainov, R 2009, Email reply prediction: a machine learning approach. in G Salvendy & MJ Smith (eds), Human Interface and the Management of Information. Information and Interaction: Symposium on Human Interface 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009, Proceedings, Part II. Springer Berlin, pp. 114-123, Symposium on Human Interface 2009, San Diego, California, United States, 19/07/09. https://doi.org/10.1007/978-3-642-02559-4_13

Email reply prediction: a machine learning approach. / Ayodele, Taiwo; Zhou, Shikun; Khusainov, Rinat.
Human Interface and the Management of Information. Information and Interaction: Symposium on Human Interface 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009, Proceedings, Part II. ed. / Gavriel Salvendy; Michael J. Smith. Springer Berlin, 2009. p. 114-123.

Research output: Chapter in Book/Report/Published conference proceeding › Conference contribution › peer-review

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N2 - Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize and prioritize their email messages, we propose a new framework; email reply prediction with unsupervised learning. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.

AB - Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize and prioritize their email messages, we propose a new framework; email reply prediction with unsupervised learning. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.

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Ayodele T, Zhou S, Khusainov R. Email reply prediction: a machine learning approach. In Salvendy G, Smith MJ, editors, Human Interface and the Management of Information. Information and Interaction: Symposium on Human Interface 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009, Proceedings, Part II. Springer Berlin. 2009. p. 114-123 doi: 10.1007/978-3-642-02559-4_13