Analysis of Mobile Service Usage Behaviour with Bayesian Belief Networks
The purpose of this paper is to identify probabilistic relationships of mobile service usage behaviour, and especially to understand the probabilistic relationship between overall service usage diversity and average daily service usage intensity. These are topical themes due to the high number of services available in application stores which may or may not lead to high usage diversity of mobile services. Four analytical methods are used in the study, all are based on Bayesian Networks; 1) Visual analysis of Bayesian Networks to find initially interesting patterns, variables and their relationships, 2) user segmentation analysis, 3) node force analysis and 4) a combination of expert-based service clustering and machine learning for usage diversity vs. intensity analysis. All the analyses were conducted with handset-based data collected from university students and staff. The analysis indicates that services exist, which mediate usage of other services. In other words, usage of these services increases the probability of using also other services. A service called Installer is an example of this kind of a service. In addition, probabilistic relationships can be found within certain service cluster pairs in their usage diversity and intensity values. Based on these relationships, similar mediation type of behaviour can be found for service clusters as for individual services. This is most visible in the relation between System/Utilities and Business/Productivity service clusters. They do not have a direct relationship but usage diversity is a mediator between them. Furthermore, segmentation analysis shows that the user segment called "experimentalists" uses more mediator services than other user segments. Furthermore, "experimentalists" use a much broader set of services daily, than the other segments. This study demonstrates that a Bayesian Network is a straightforward way to express model characteristics on high level. Moreover, Node Force, Direct and Total effect are useful metrics to measure the mediation effects. The clustering implemented as a hybrid of machine learning and expert-based clustering process is also a useful way to calculate relationships between clusters of more than a hundred individual services.
mobile services bayesian networks segmentation clustering machine learning handset-based data