PURL: Periodic user representation learning from temporal event records for personalized health management

Yu Huang, Chih Ling Hsu, Vincent Shin-Mu Tseng*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Activity logging applications for mobile health have gained wide attentions recently and tracking of user health states is becoming an emerging trend in human daily life. Modeling user behaviors from users' temporal event records is a promising research topic for enhancing the practicability of personal health management. However, user modeling has long been a very challenging problem due to the complexity in activities of each individual, which consists of two key sub-issues, periodicity and diversity. In this paper, we propose a novel user modeling approach, namely Periodic User Representation Learning (PURL), to learn dynamic representations of user behaviors from large-scale temporal event records. To deal with the periodicity issue, we utilize periodic frequent pattern mining to capture the periodic user behaviors from users' temporal event records. Next, to cope with the diversity of user behaviors, we further build a periodic temporal pattern embedding module, which yields interpretable user representations according to the presence of each distinct periodic temporal pattern. To the best of our knowledge, PURL is the first user behavior modeling method that tackles the periodicity and diversity issues of users' temporal event records. Based on a real-world large-scale user activity logging dataset, experimental results demonstrate that PURL delivers up to 47% improvement in terms of the accuracy on health state prediction task compared with other existing methods.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
EditorsWookey Lee, Luonan Chen, Yang-Sae Moon, Julien Bourgeois, Mehdi Bennis, Yu-Feng Li, Young-Guk Ha, Hyuk-Yoon Kwon, Alfredo Cuzzocrea
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages358-365
Number of pages8
ISBN (Electronic)9781728160344
DOIs
StatePublished - Feb 2020
Event2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 - Busan, Korea, Republic of
Duration: 19 Feb 202022 Feb 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020

Conference

Conference2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
CountryKorea, Republic of
CityBusan
Period19/02/2022/02/20

Keywords

  • Periodic pattern mining
  • Personalized health management
  • Temporal events
  • User modeling

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