Effective routine behavior pattern discovery from sparse mobile phone data via collaborative filtering

Jiangchuan Zheng, Siyuan Liu, Lionel M. Ni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Recognizing and classifying users' routine behavior patterns from sensor data has been a hot topic in pervasive computing. Its objective is to automatically discover recurrent routine patterns in a user's daily life by leveraging the multimodal data generated from wearable sensors such as mobile phones. This kind of knowledge can be utilized in many ways such as identifying similar users in terms of their behaviors, providing behavior contexts to enable advanced human-centered applications, etc. While numerous works have been done in this area, most of them rely on densely sampled mobile data collected from specially-programmed sensors that can "follow" people throughout the day. In this paper, we study how to achieve the same objective when the mobile data presented is much sparser, such as traditional mobile phone data where a user's location is reported only when he makes a call. Although a single user's mobile data is far from sufficient to reveal his characteristic behavior, we show that when exploiting a large number of users' mobile data in a principled collaborative way which facilitate similar users' data to complement each other, representative routine patterns can be revealed and each user can be characterized properly. Experiments on synthetic and real mobile phone data set demonstrate the effectiveness of our methods, and also show our model's ability in predicting human activity using the patterns learned.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Pervasive Computing and Communications, PerCom 2013
Pages29-37
Number of pages9
DOIs
StatePublished - Jul 18 2013
Event11th IEEE International Conference on Pervasive Computing and Communications, PerCom 2013 - San Diego, CA, United States
Duration: Mar 18 2013Mar 22 2013

Publication series

Name2013 IEEE International Conference on Pervasive Computing and Communications, PerCom 2013

Other

Other11th IEEE International Conference on Pervasive Computing and Communications, PerCom 2013
CountryUnited States
CitySan Diego, CA
Period3/18/133/22/13

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Cite this

Zheng, J., Liu, S., & Ni, L. M. (2013). Effective routine behavior pattern discovery from sparse mobile phone data via collaborative filtering. In 2013 IEEE International Conference on Pervasive Computing and Communications, PerCom 2013 (pp. 29-37). [6526711] (2013 IEEE International Conference on Pervasive Computing and Communications, PerCom 2013). https://doi.org/10.1109/PerCom.2013.6526711