AppsPred: Predicting Context-Aware Smartphone Apps using Random Forest Learning

TL;DR

This paper introduces AppsPred, a context-aware smartphone app prediction model using Random Forests, which effectively predicts personalized app usage based on multi-dimensional contexts, outperforming other classifiers.

Contribution

The paper presents a novel Random Forest-based model, AppsPred, for personalized app prediction considering diverse contextual factors, demonstrating superior performance over existing methods.

Findings

AppsPred outperforms ZeroR, Naive Bayes, Decision Tree, SVM, and Logistic Regression.

The model effectively incorporates multi-dimensional contexts for personalized predictions.

Experimental results validate the model's high accuracy in real-world datasets.

Abstract

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.