Article

A hybrid learning system for recognizing user tasks from desktop activities and email messages

Authors:

Jianqiang Shen,

Thomas G. Dietterich,

Jonathan L. HerlockerAuthors Info & Claims

IUI '06: Proceedings of the 11th international conference on Intelligent user interfaces

Pages 86 - 92

https://doi.org/10.1145/1111449.1111473

Published: 29 January 2006 Publication History

Abstract

The TaskTracer system seeks to help multi-tasking users manage the resources that they create and access while carrying out their work activities. It does this by associating with each user-defined activity the set of files, folders, email messages, contacts, and web pages that the user accesses when performing that activity. The initial TaskTracer system relies on the user to notify the system each time the user changes activities. However, this is burdensome, and users often forget to tell TaskTracer what activity they are working on. This paper introduces TaskPredictor, a machine learning system that attempts to predict the user's current activity. TaskPredictor has two components: one for general desktop activity and another specifically for email. TaskPredictor achieves high prediction precision by combining three techniques: (a) feature selection via mutual information, (b) classification based on a confidence threshold, and (c) a hybrid design in which a Naive Bayes classifier estimates the classification confidence but where the actual classification decision is made by a support vector machine. This paper provides experimental results on data collected from TaskTracer users.

References

[1]

Y. Altun, I. Tsochantaridis, and T. Hofmann. Hidden Markov support vector machines. In Proc. of ICML-03.

[2]

S. Andrews, L. Cai, D. Gondek, A. Greenwald, D. Grollman, A. M. Jonsson, K. Hall, M. Lease, B. Ng, J. Raiti, V. Sweetser, and J. Turner. Astrology: the study of astro teller. In ICML04 Workshop Physiological Data Modeling - A Competition, 2004.

[3]

J. D. Brutlag and C. Meek. Challenges of the email domain for text classification. In Proc.of ICML-00.

Digital Library

[4]

W. W. Cohen. Learning rules that classify e-mail. In Proc. Of the 1996 AAAI Spring Symposium in Information Access, 1996.

[5]

A. N. Dragunov, T. G. Dietterich, K. Johnsrude, M. McLaughlin, L. Li, and J. L. Herlocker. Tasktracer: A desktop environment to support multi-tasking knowledge workers. In Proc. of IUI-05.

Digital Library

[6]

T. Fawcett and F. Provost. Activity monitoring: notice interesting changes in behavior. In Proc. of KDD-99, 1999.

Digital Library

[7]

K. Haigh and H. A. Yanco. Automation as caregiver: a survey of issues and technologies. In AAAI workshop on Automation as Caregiver, 2002.

[8]

E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and K. Rommelse. The lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Proc. of UAI-98, 1998.

Digital Library

[9]

E. Horvitz, A. Jacobs, and D. Hovel. Learning and reasoning about interruption. In Proc. of ICMI-03.

Digital Library

[10]

E. Horvitz, A. Jacobs, and D. Hovel. Attention-sensitive alerting. In Proc. of UAI-99, 1999.

Digital Library

[11]

T. Joachims. Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publishers, 2001.

Digital Library

[12]

B. Klimt and Y. Yang. The enron corpus: A new dataset for email classification research. In Proc. of ECML2004, 2004.

Digital Library

[13]

A. McCallum and G. Huang. Automatic categorization of email into folders: Benchmark experiments on enron and sri corpora. Technical Report IR-418, CIIR, 2004.

[14]

M. Philipose, K. Fishkin, M. Perkowitz, D. Patterson, and D. Hahnel. The probabilistic activity toolkit: Towards enabling activity-aware computer interfaces. Technical Report IRS-TR-03-013, Intel Research Lab, Seattle, WA, 2003.

[15]

M. Porter. An algorithm for suffix stripping. Program, 14(3):130--137, 1980.

[16]

R. Segal and J. Kephart. Mailcat: an intelligent assistant for organizing e-mail. In Proc. of the Third ICAA, 1999.

Digital Library

[17]

P. Smyth, D. Heckerman, and M. I. Jordan. Probabilistic independence networks for hidden markov probability models. Neural Computation, 9(2):227--269, 1997.

Digital Library

[18]

T.-F. Wu, C.-J. Lin, and R. C. Weng. Probability estimates for multi-class classification by pairwise coupling. In Advances in NIPS 16.

[19]

Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. In Proc. of ICML-97.

Digital Library

Cited By

Wu ZTan JWu HZha W(2025)Generative adversarial network model-based knowledge recommendation with knowledge graph in product designJournal of Engineering Design10.1080/09544828.2025.2450764(1-31)Online publication date: 13-Feb-2025
https://doi.org/10.1080/09544828.2025.2450764
R. Yousefi ZVuong TAlGhossein MRuotsalo TJaccuci GKaski S(2024)Entity Footprinting: Modeling Contextual User States via Digital Activity MonitoringACM Transactions on Interactive Intelligent Systems10.1145/364389314:2(1-27)Online publication date: 5-Feb-2024
https://dl.acm.org/doi/10.1145/3643893
Meyer ASatterfield CZuger MKevic KMurphy GZimmermann TFritz T(2022)Detecting Developers’ Task Switches and TypesIEEE Transactions on Software Engineering10.1109/TSE.2020.298408648:1(225-240)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TSE.2020.2984086
Show More Cited By

Index Terms

A hybrid learning system for recognizing user tasks from desktop activities and email messages
1. Applied computing
  1. Enterprise computing
    1. Business process management
2. Social and professional topics
  1. Professional topics
    1. Computing and business
      1. Automation

Recommendations

Using phrases as features in email classification

In this paper, we report our experience on the use of phrases as basic features in the email classification problem. We performed extensive empirical evaluation using our large email collections and tested with three text classification algorithms, ...
New under-sampling methods to address the problem of unbalanced sentiment classification: application on Arabic datasets

This paper presents the study we have carried out to address the problem of unbalanced datasets in supervised sentiment classification in an Arabic context. We propose three different methods to under-sample the majority class documents. Our goal is to ...
Automatic Punjabi poetry classification using machine learning algorithms with reduced feature set

With the appearance of Unicode encoding, content in Indian dialects is continually expanding on the internet. Gathering of artistic writings in Punjabi language, particularly poetry, is expanding day by day on the web. In this way, grouping of poems, as ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IUI '06: Proceedings of the 11th international conference on Intelligent user interfaces

January 2006

392 pages

ISBN:1595932879

DOI:10.1145/1111449

General Chairs:
Ernest Edmonds
University of Technology Sydney, Australia
,
Doug Riecken
IBM Watson Research, USA
,
Program Chairs:
Cécile L. Paris
CSIRO ICT Centre, Australia
,
Candace L. Sidner
MERL, USA

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 January 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

IUI06

Sponsor:

IUI06: 11th International Conference on Intelligent User Interfaces

January 29 - February 1, 2006

Sydney, Australia

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

73
Total Citations
View Citations
979
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wu ZTan JWu HZha W(2025)Generative adversarial network model-based knowledge recommendation with knowledge graph in product designJournal of Engineering Design10.1080/09544828.2025.2450764(1-31)Online publication date: 13-Feb-2025
https://doi.org/10.1080/09544828.2025.2450764
R. Yousefi ZVuong TAlGhossein MRuotsalo TJaccuci GKaski S(2024)Entity Footprinting: Modeling Contextual User States via Digital Activity MonitoringACM Transactions on Interactive Intelligent Systems10.1145/364389314:2(1-27)Online publication date: 5-Feb-2024
https://dl.acm.org/doi/10.1145/3643893
Meyer ASatterfield CZuger MKevic KMurphy GZimmermann TFritz T(2022)Detecting Developers’ Task Switches and TypesIEEE Transactions on Software Engineering10.1109/TSE.2020.298408648:1(225-240)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TSE.2020.2984086
Pellegrin FYücel ZMonden ALeelaprute P(2021)Task estimation for software company employees based on computer interaction logsEmpirical Software Engineering10.1007/s10664-021-10006-426:5Online publication date: 13-Jul-2021
https://doi.org/10.1007/s10664-021-10006-4
Trippas JSpina DScholer FAwadallah ABailey PBennett PWhite RLiono JRen YSalim FSanderson MAzzopardi LHalvey MRuthven IJoho HMurdock VQvarfordt P(2019)Learning About Work Tasks to Inform Intelligent Assistant DesignProceedings of the 2019 Conference on Human Information Interaction and Retrieval10.1145/3295750.3298934(5-14)Online publication date: 8-Mar-2019
https://dl.acm.org/doi/10.1145/3295750.3298934
Jones PSharma SMoon CSamatova NPapadopoulos GKuflik TChen FDuarte CFu W(2017)A Network-Fusion Guided Dashboard Interface for Task-Centric Document CurationProceedings of the 22nd International Conference on Intelligent User Interfaces10.1145/3025171.3025177(481-491)Online publication date: 7-Mar-2017
https://dl.acm.org/doi/10.1145/3025171.3025177
Bota HBennett PAwadallah ADumais SNordlie RPharo NFreund LLarsen BRussel D(2017)Self-EsProceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval10.1145/3020165.3020189(205-214)Online publication date: 7-Mar-2017
https://dl.acm.org/doi/10.1145/3020165.3020189
Berry PDonneau-Golencer TDuong KGervasio MPeintner BYorke-Smith N(2017)Evaluating intelligent knowledge systemsKnowledge and Information Systems10.1007/s10115-016-1011-352:2(379-409)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s10115-016-1011-3
Sappelli MVerberne SKraaij W(2016)Adapting the Interactive Activation Model for Context Recognition and IdentificationACM Transactions on Interactive Intelligent Systems10.1145/28730676:3(1-30)Online publication date: 14-Sep-2016
https://dl.acm.org/doi/10.1145/2873067
Seeliger ASchmidt BSchweizer IMühlhäuser MNichols JMahmud JO'Donovan JConati CZancanaro M(2016)What Belongs Together Comes TogetherProceedings of the 21st International Conference on Intelligent User Interfaces10.1145/2856767.2856777(60-70)Online publication date: 7-Mar-2016
https://dl.acm.org/doi/10.1145/2856767.2856777
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten