research-article

MAPer: A Multi-scale Adaptive Personalized Model for Temporal Human Behavior Prediction

Authors:

Sarah Masud Preum,

John A. Stankovic,

Yanjun QiAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 433 - 442

https://doi.org/10.1145/2806416.2806562

Published: 17 October 2015 Publication History

Abstract

The primary objective of this research is to develop a simple and interpretable predictive framework to perform temporal modeling of individual user's behavior traits based on each person's past observed traits/behavior. Individual-level human behavior patterns are possibly influenced by various temporal features (e.g., lag, cycle) and vary across temporal scales (e.g., hour of the day, day of the week). Most of the existing forecasting models do not capture such multi-scale adaptive regularity of human behavior or lack interpretability due to relying on hidden variables. Hence, we build a multi-scale adaptive personalized (MAPer) model that quantifies the effect of both lag and behavior cycle for predicting future behavior. MAper includes a novel basis vector to adaptively learn behavior patterns and capture the variation of lag and cycle across multi-scale temporal contexts. We also extend MAPer to capture the interaction among multiple behaviors to improve the prediction performance.

We demonstrate the effectiveness of MAPer on four real datasets representing different behavior domains, including, habitual behavior collected from Twitter, need based behavior collected from search logs, and activities of daily living collected from a single resident and a multi-resident home. Experimental results indicate that MAPer significantly improves upon the state-of-the-art and baseline methods and at the same time is able to explain the temporal dynamics of individual-level human behavior.

References

[1]

F. Abel, Q. Gao, G.-J. Houben, and K. Tao. Analyzing user modeling on twitter for personalized news recommendations. In User Modeling, Adaption and Personalization, pages 1--12. Springer, 2011.

Digital Library

[2]

E. Adar, D. S. Weld, B. N. Bershad, and S. S. Gribble. Why we search: visualizing and predicting user behavior. In Proceedings of the 16th international conference on World Wide Web (WWW), pages 161--170. ACM, 2007.

Digital Library

[3]

H. Alerndar, H. Ertan, O. D. Incel, and C. Ersoy. Aras human activity datasets in multiple homes with multiple residents. In IEEE PervasiveHealth, 2013.

[4]

F. Baskaya, H. Keskustalo, and K. Järvelin. Modeling behavioral factors ininteractive information retrieval. In Proceedings of the 22nd ACM international conference on information & knowledge management (CIKM), pages 2297--2302. ACM, 2013.

Digital Library

[5]

C. Borgelt. Frequent item set mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(6):437--456, 2012.

Digital Library

[6]

Y. Chen, D. Pavlov, and J. F. Canny. Large-scale behavioral targeting. In ACM SIGKDD, 2009.

Digital Library

[7]

Z. Cheng, J. Caverlee, and K. Lee. You are where you tweet: a content-based approach to geo-locating twitter users. In ACM SIGKDD, 2010.

Digital Library

[8]

M. Clements and D. Hendry. Forecasting economic time series. Cambridge University Press, 1998.

[9]

J. F. Desforges, W. B. Applegate, J. P. Blass, and T. F. Williams. Instruments for the functional assessment of older patients. New England Journal of Medicine, 322(17):1207--1214, 1990.

[10]

H. Gao, J. Tang, X. Hu, and H. Liu. Modeling temporal effects of human mobile behavior on location-based social networks. In ACM CIKM, 2013.

Digital Library

[11]

S. A. Golder, D. M. Wilkinson, and B. A. Huberman. Rhythms of social interaction: Messaging within a massive online network. In Communities and Technologies 2007, pages 41--66. Springer, 2007.

[12]

J. D. Hamilton. Time series analysis, volume 2. Princeton university press Princeton, 1994.

[13]

E. Hoque, R. F. Dickerson, S. M. Preum, M. Hanson, A. Barth, and J. A. Stankovic. Holmes: A comprehensive anomaly detection system for daily in-home activities. In Proceedings of the 11th IEEE international conference on distributed computing in sensor systems, 2015.

Digital Library

[14]

R. J. Hyndman, Y. Khandakar, et al. Automatic time series for forecasting: the forecast package for r. Technical report, Monash University, Department of Econometrics and Business Statistics, 2007.

[15]

M. G. Kendall and J. K. Ord. Time-series, volume 296. Edward Arnold London, 1990.

[16]

J. R. Kwapisz, G. M. Weiss, and S. A. Moore. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter, 12(2):74--82, 2011.

Digital Library

[17]

J. Liu, Y. Liu, M. Zhang, and S. Ma. How do users grow up along with search engines?: a study of long-term users' behavior. In ACM CIKM, 2013.

Digital Library

[18]

H. Lütkepohl. Vector autoregressive models. Springer, 2011.

[19]

M. Naaman, A. X. Zhang, S. Brody, and G. Lotan. On the study of diurnal urban routines on twitter. In ICWSM, 2012.

[20]

E. Nazerfard and D. J. Cook. Using bayesian networks for daily activity prediction. In AAAI Workshop: Plan, Activity, and Intent Recognition, 2013.

Digital Library

[21]

G. Pass, A. Chowdhury, and C. Torgeson. A picture of search. In InfoScale, volume 152, page 1. Citeseer, 2006.

Digital Library

[22]

K. Radinsky, K. Svore, S. Dumais, J. Teevan, A. Bocharov, and E. Horvitz. Modeling and predicting behavioral dynamics on the web. In ACM WWW, 2012.

Digital Library

[23]

J. Scott, A. Bernheim Brush, J. Krumm, B. Meyers, M. Hazas, S. Hodges, and N. Villar. Preheat: controlling home heating using occupancy prediction. In Proceedings of the 13th international conference on Ubiquitous computing, pages 281--290. ACM, 2011.

Digital Library

[24]

P. V. Senin. Literature review on time series indexing. 2009.

[25]

W. Weerkamp and M. De Rijke. Activity prediction: A twitter-based exploration. In SIGIR Workshop on Time-aware Information Access, 2012.

[26]

M. Ylvisaker and T. J. Feeney. Collaborative brain injury intervention: Positive everyday routines. Singular Publishing Group, 1998.

[27]

Y. Zhu, E. Zhong, S. J. Pan, X. Wang, M. Zhou, and Q. Yang. Predicting user activity level in social networks. In ACM CIKM, 2013.

Digital Library

Cited By

Zhou DYu HYu JZhao SXu WLi QCai F(2024)MFDS-STGCN: Predicting the Behaviors of College Students With Fine-Grained Spatial-Temporal Activities DataIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.334413112:1(254-265)Online publication date: Jan-2024
https://doi.org/10.1109/TETC.2023.3344131
Preum SMunir SMa MYasar MStone DWilliams RAlemzadeh HStankovic J(2021)A Review of Cognitive Assistants for HealthcareACM Computing Surveys10.1145/341936853:6(1-37)Online publication date: 2-Feb-2021
https://dl.acm.org/doi/10.1145/3419368
Zheng YXie JVo TLee BAjisafe T(2019)Predicting Daily Physical Activity Level for Older Adults Using Wearable Activity TrackersHuman Aspects of IT for the Aged Population. Social Media, Games and Assistive Environments10.1007/978-3-030-22015-0_47(602-614)Online publication date: 8-Jun-2019
https://doi.org/10.1007/978-3-030-22015-0_47
Show More Cited By

Index Terms

MAPer: A Multi-scale Adaptive Personalized Model for Temporal Human Behavior Prediction
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Copyright © 2015 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]

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Published: 17 October 2015

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Zhou DYu HYu JZhao SXu WLi QCai F(2024)MFDS-STGCN: Predicting the Behaviors of College Students With Fine-Grained Spatial-Temporal Activities DataIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.334413112:1(254-265)Online publication date: Jan-2024
https://doi.org/10.1109/TETC.2023.3344131
Preum SMunir SMa MYasar MStone DWilliams RAlemzadeh HStankovic J(2021)A Review of Cognitive Assistants for HealthcareACM Computing Surveys10.1145/341936853:6(1-37)Online publication date: 2-Feb-2021
https://dl.acm.org/doi/10.1145/3419368
Zheng YXie JVo TLee BAjisafe T(2019)Predicting Daily Physical Activity Level for Older Adults Using Wearable Activity TrackersHuman Aspects of IT for the Aged Population. Social Media, Games and Assistive Environments10.1007/978-3-030-22015-0_47(602-614)Online publication date: 8-Jun-2019
https://doi.org/10.1007/978-3-030-22015-0_47
Li SGao XBao WChen GLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)FM-HawkesProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132883(1119-1128)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132883
Lv JLiu WZhang MGong HWu BMa HLiu QLienhart RWang HChen SBoll SChen PFriedland GLi JYan S(2017)Multi-feature Fusion for Predicting Social Media PopularityProceedings of the 25th ACM international conference on Multimedia10.1145/3123266.3127897(1883-1888)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3123266.3127897
Wu BCheng WZhang YMei THanjalic ASnoek CWorring MBulterman DHuet BKelliher AKompatsiaris YLi J(2016)Time MattersProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2964335(1336-1344)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1145/2964284.2964335

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