  Due to the popularity of context-awareness in the Internet of Things (IoT)
and the recent advanced features in the most popular IoT device, i.e.,
smartphone, modeling and predicting personalized usage behavior based on
relevant contexts can be highly useful in assisting them to carry out daily
routines and activities. Usage patterns of different categories smartphone apps
such as social networking, communication, entertainment, or daily life services
related apps usually vary greatly between individuals. People use these apps
differently in different contexts, such as temporal context, spatial context,
individual mood and preference, work status, Internet connectivity like Wifi?
status, or device related status like phone profile, battery level etc. Thus,
we consider individuals' apps usage as a multi-class context-aware problem for
personalized modeling and prediction. Random Forest learning is one of the most
popular machine learning techniques to build a multi-class prediction model.
Therefore, in this paper, we present an effective context-aware smartphone apps
prediction model, and name it "AppsPred" using random forest machine learning
technique that takes into account optimal number of trees based on such
multi-dimensional contexts to build the resultant forest. The effectiveness of
this model is examined by conducting experiments on smartphone apps usage
datasets collected from individual users. The experimental results show that
our AppsPred significantly outperforms other popular machine learning
classification approaches like ZeroR, Naive Bayes, Decision Tree, Support
Vector Machines, Logistic Regression while predicting smartphone apps in
various context-aware test cases.
