Personalized Mobile App Experience: Using Contextual Information to Enhance User Interactions

Abstract

The explosive development of mobile applications has led to the necessity of increased personalization and context awareness of the user experiences. Contextual background is crucial in the formation of these experiences in terms of location, time and user preferences as well. The paper will also research how contextual data can be used to personalize interaction and how it is achieved in mobile applications by providing a particular example of improving the level of engagement and satisfaction of people using the mobile application. The study tries to understand how machine learning models, especially decision trees and clustering algorithms are used in contextual information processing and analysis. By addressing the behavior patterns of users, across the different types of apps such as social media, fitness, and e-commerce, the research will illustrate that personalized functionalities (customized content, notifications, and recommendations) of the app may greatly enhance peoples interaction. The findings show that applications that make use of contextual information beat those that do not in activation and retention of users. The implications of these findings are wider in the context of mobile apps design and development since it has been shown that the implementation of context-aware features can significantly improve user experience and lead to a successful app.