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

Abstract

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.