Reading scheduler: proactive recommendations to help users cope with their daily reading volume

Tilman Dingler; Benjamin Tag; Sabrina Lehrer; Albrecht Schmidt

doi:10.1145/3282894.3282917

Reading scheduler: proactive recommendations to help users cope with their daily reading volume

Tilman Dingler, Benjamin Tag, Sabrina Lehrer, Albrecht Schmidt

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

11 Citations (Scopus)

Abstract

To help deal with daily reading volumes, we present Reading Scheduler, a smartphone application linked to people’s reading list, which triggers reading reminders throughout the day. The app suggests articles according to their length, complexity, and the time available for reading as indicated by the user. In a field study, we collected usage data from ten participants over the course of two weeks. During this time, we recorded mobile sensor data and trained a classifier to detect opportune moments for reading. Participants read 182 articles while we collected 787,752 sensor data points. Together with an assessment of the feasibility of proactive reading suggestions, we present a prediction model with close to 73% accuracy, that can be used to build mobile recommender systems for utilizing idle moments for reading throughout the user’s day.

Original language	English
Title of host publication	17 th International Conference on Mobile and Ubiquitous Multimedia, Proceedings
Editors	Slim Abdennadher, Florian Alt
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	239-244
Number of pages	6
ISBN (Electronic)	9781450365949
DOIs	https://doi.org/10.1145/3282894.3282917
Publication status	Published - 2018
Externally published	Yes
Event	Mobile and Ubiquitous Multimedia (ACM) 2018 - Cairo, Egypt Duration: 25 Nov 2018 → 28 Nov 2018 Conference number: 17th https://dl.acm.org/doi/proceedings/10.1145/3282894 (Proceedings) https://www.mum-conf.org/2018/index.php (Website)

Conference

Conference	Mobile and Ubiquitous Multimedia (ACM) 2018
Abbreviated title	MUM 2018
Country/Territory	Egypt
City	Cairo
Period	25/11/18 → 28/11/18
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3282894 (Proceedings) https://www.mum-conf.org/2018/index.php (Website)

Keywords

Mobile Reading
Reading Context
Reading Scheduler

Access to Document

10.1145/3282894.3282917

Cite this

@inproceedings{df313cb1f87f425ca0cb421fef09d129,

title = "Reading scheduler: proactive recommendations to help users cope with their daily reading volume",

abstract = "To help deal with daily reading volumes, we present Reading Scheduler, a smartphone application linked to people{\textquoteright}s reading list, which triggers reading reminders throughout the day. The app suggests articles according to their length, complexity, and the time available for reading as indicated by the user. In a field study, we collected usage data from ten participants over the course of two weeks. During this time, we recorded mobile sensor data and trained a classifier to detect opportune moments for reading. Participants read 182 articles while we collected 787,752 sensor data points. Together with an assessment of the feasibility of proactive reading suggestions, we present a prediction model with close to 73% accuracy, that can be used to build mobile recommender systems for utilizing idle moments for reading throughout the user{\textquoteright}s day.",

keywords = "Mobile Reading, Reading Context, Reading Scheduler",

author = "Tilman Dingler and Benjamin Tag and Sabrina Lehrer and Albrecht Schmidt",

note = "Funding Information: This work is supported by the Future and Emerging Technologies of the European Commission under grant number 612933 and by JST CREST under grant number JPMJCR16E1. Publisher Copyright: {\textcopyright} 2018 ACM. ISBN 978-1-4503-6594-9/18/11. . . $15.00; Mobile and Ubiquitous Multimedia (ACM) 2018, MUM 2018 ; Conference date: 25-11-2018 Through 28-11-2018",

year = "2018",

doi = "10.1145/3282894.3282917",

language = "English",

pages = "239--244",

editor = "Slim Abdennadher and Florian Alt",

booktitle = "17 th International Conference on Mobile and Ubiquitous Multimedia, Proceedings",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

url = "https://dl.acm.org/doi/proceedings/10.1145/3282894, https://www.mum-conf.org/2018/index.php",

}

Dingler, T, Tag, B, Lehrer, S & Schmidt, A 2018, Reading scheduler: proactive recommendations to help users cope with their daily reading volume. in S Abdennadher & F Alt (eds), 17 th International Conference on Mobile and Ubiquitous Multimedia, Proceedings. Association for Computing Machinery (ACM), New York NY USA, pp. 239-244, Mobile and Ubiquitous Multimedia (ACM) 2018, Cairo, Egypt, 25/11/18. https://doi.org/10.1145/3282894.3282917

Reading scheduler: proactive recommendations to help users cope with their daily reading volume. / Dingler, Tilman; Tag, Benjamin; Lehrer, Sabrina et al.
17 th International Conference on Mobile and Ubiquitous Multimedia, Proceedings. ed. / Slim Abdennadher; Florian Alt. New York NY USA: Association for Computing Machinery (ACM), 2018. p. 239-244.

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

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T1 - Reading scheduler

T2 - Mobile and Ubiquitous Multimedia (ACM) 2018

AU - Dingler, Tilman

AU - Tag, Benjamin

AU - Lehrer, Sabrina

AU - Schmidt, Albrecht

N1 - Conference code: 17th

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N2 - To help deal with daily reading volumes, we present Reading Scheduler, a smartphone application linked to people’s reading list, which triggers reading reminders throughout the day. The app suggests articles according to their length, complexity, and the time available for reading as indicated by the user. In a field study, we collected usage data from ten participants over the course of two weeks. During this time, we recorded mobile sensor data and trained a classifier to detect opportune moments for reading. Participants read 182 articles while we collected 787,752 sensor data points. Together with an assessment of the feasibility of proactive reading suggestions, we present a prediction model with close to 73% accuracy, that can be used to build mobile recommender systems for utilizing idle moments for reading throughout the user’s day.

AB - To help deal with daily reading volumes, we present Reading Scheduler, a smartphone application linked to people’s reading list, which triggers reading reminders throughout the day. The app suggests articles according to their length, complexity, and the time available for reading as indicated by the user. In a field study, we collected usage data from ten participants over the course of two weeks. During this time, we recorded mobile sensor data and trained a classifier to detect opportune moments for reading. Participants read 182 articles while we collected 787,752 sensor data points. Together with an assessment of the feasibility of proactive reading suggestions, we present a prediction model with close to 73% accuracy, that can be used to build mobile recommender systems for utilizing idle moments for reading throughout the user’s day.

KW - Mobile Reading

KW - Reading Context

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BT - 17 th International Conference on Mobile and Ubiquitous Multimedia, Proceedings

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ER -

Reading scheduler: proactive recommendations to help users cope with their daily reading volume

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Conference

Keywords

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