PrefMiner: Mining User’s Preferences for Intelligent Mobile Notification Management

Abhinav Mehrotra; Robert Hendley; Mirco Musolesi

doi:10.1145/2971648.2971747

PrefMiner: Mining User’s Preferences for Intelligent Mobile Notification Management

Abhinav Mehrotra, Robert Hendley, Mirco Musolesi

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

65 Citations (Scopus)

433 Downloads (Pure)

Abstract

Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users’ interests and, also
for this reason, they are considered disruptive and potentially annoying.

In this paper we present the design, implementation and evaluation of PrefMiner, a novel interruptibility management solution that learns users’ preferences for receiving notifications based on automatic extraction of rules by mining their interaction with mobile phones. The goal is to build a system that is intelligible for users, i.e., not just a “black-box” solution. Rules are shown to users who might decide to accept or discard them at run-time. The design of PrefMiner is based on a large scale mobile notification dataset and its effectiveness is evaluated by means of an in-the-wild deployment.

Original language	English
Title of host publication	Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016)
Publisher	Association for Computing Machinery
Pages	1223-1234
Number of pages	12
DOIs	https://doi.org/10.1145/2971648.2971747
Publication status	Published - 17 Sept 2016
Event	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) - Heidelberg, Germany Duration: 12 Sept 2016 → 16 Sept 2016

Conference

Conference	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016)
Country/Territory	Germany
City	Heidelberg
Period	12/09/16 → 16/09/16

Access to Document

10.1145/2971648.2971747

Mehrotra_et_al_PrefMiner_Mining_User's_Preferences
© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Accepted author manuscript, 0.98 MBLicence: None: All rights reserved

MACACO: Mobile context-Adaptive CAching for COntent-centric networking
Musolesi, M.
Engineering & Physical Science Research Council
30/04/14 → 29/04/17
Project: Research Councils

Cite this

@inproceedings{c3025913a2b249e6913f3f310b148f68,

title = "PrefMiner: Mining User{\textquoteright}s Preferences for Intelligent Mobile Notification Management",

abstract = "Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users{\textquoteright} interests and, alsofor this reason, they are considered disruptive and potentially annoying.In this paper we present the design, implementation and evaluation of PrefMiner, a novel interruptibility management solution that learns users{\textquoteright} preferences for receiving notifications based on automatic extraction of rules by mining their interaction with mobile phones. The goal is to build a system that is intelligible for users, i.e., not just a “black-box” solution. Rules are shown to users who might decide to accept or discard them at run-time. The design of PrefMiner is based on a large scale mobile notification dataset and its effectiveness is evaluated by means of an in-the-wild deployment.",

author = "Abhinav Mehrotra and Robert Hendley and Mirco Musolesi",

year = "2016",

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day = "17",

doi = "10.1145/2971648.2971747",

language = "English",

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booktitle = "Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016)",

publisher = "Association for Computing Machinery ",

note = "2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) ; Conference date: 12-09-2016 Through 16-09-2016",

}

Mehrotra, A, Hendley, R & Musolesi, M 2016, PrefMiner: Mining User’s Preferences for Intelligent Mobile Notification Management. in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) . Association for Computing Machinery , pp. 1223-1234 , 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) , Heidelberg, Germany, 12/09/16. https://doi.org/10.1145/2971648.2971747

PrefMiner: Mining User’s Preferences for Intelligent Mobile Notification Management. / Mehrotra, Abhinav; Hendley, Robert; Musolesi, Mirco.
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) . Association for Computing Machinery , 2016. p. 1223-1234 .

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - PrefMiner: Mining User’s Preferences for Intelligent Mobile Notification Management

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AU - Musolesi, Mirco

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AB - Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users’ interests and, alsofor this reason, they are considered disruptive and potentially annoying.In this paper we present the design, implementation and evaluation of PrefMiner, a novel interruptibility management solution that learns users’ preferences for receiving notifications based on automatic extraction of rules by mining their interaction with mobile phones. The goal is to build a system that is intelligible for users, i.e., not just a “black-box” solution. Rules are shown to users who might decide to accept or discard them at run-time. The design of PrefMiner is based on a large scale mobile notification dataset and its effectiveness is evaluated by means of an in-the-wild deployment.

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M3 - Conference contribution

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PrefMiner: Mining User’s Preferences for Intelligent Mobile Notification Management

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MACACO: Mobile context-Adaptive CAching for COntent-centric networking

Cite this