Investigation of relationships between high-level user contexts and mobile application usage

Investigation of relationships between high-level user contexts and mobile application usage

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Along with the widely spreading of smartphones, users leverage various functions of the smartphones in their everyday life. To reveal the behavior of smartphone users, many existing works collect low-level contexts such as location and movement status of users from sensors (e.g., GPS, acceleration sensor) to predict the users' situations when they use smartphones. However, it seems that not only low-level contexts but also high-level contexts (e.g., how busy, how good in health, working/day off, and with whom the user is) have significant impact on smartphone users' behavior. In our previous work, we developed a log-collection system to collect high-level contexts by questioning users directly. In this system, to collect a large amount of logs from general smartphone users from whom we have adopted a game-based approach. So far, we have collected approximately 0.7 millions of logs from about 400 users. In this chapter, we investigate relationships between high-level user contexts and application usage by analyzing a large amount of application usage logs collected through this system. Specifically, we report our experiments which have conducted association rule mining on the collected logs and show some findings. Our study described in this chapter will be a guideline on how to collect big data on user's high-level contexts, and how to apply them for important context-aware applications such as application recommendation.

Chapter Contents:

  • 18.1 Introduction
  • 18.2 Related work
  • 18.2.1 Investigation of mobile user's behavior
  • 18.2.2 Collecting application usage logs
  • 18.2.3 Collecting context information
  • 18.3 Log-collection system
  • 18.3.1 Initialization of the system
  • 18.3.2 Questions about contexts
  • 18.3.3 Collection of application usage logs
  • 18.3.4 Game-based approach
  • Experience point
  • Evolution
  • 18.4 Collected logs
  • 18.4.1 High-level contexts
  • 18.4.2 Application usage frequency
  • 18.4.3 Tendency of application usage by time
  • 18.5 Relationships between applications and contexts
  • 18.5.1 Characteristic rules
  • Most frequent situations
  • Specific usage
  • 18.5.2 Effect of single context
  • 18.5.3 Effect of combination of contexts
  • 18.6 Discussion
  • 18.6.1 Impacts of collecting high- level contexts
  • 18.6.2 Possible applications of high-level contexts
  • 18.7 Conclusion
  • References

Inspec keywords: mobile computing; data mining; smart phones; Big Data

Other keywords: log-collection system; game-based approach; big data; location status; mobile application usage; movement status; high-level user contexts; association rule mining; smartphone users; smartphones; context-aware applications

Subjects: Ubiquitous and pervasive computing; Knowledge engineering techniques; Data handling techniques

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