Mobile Applications Usage Prediction for Enhanced User Experience
DOI:
https://doi.org/10.25098/5.2.15Keywords:
User Experience, Mobile Applications Usage, Machine Learning, UX, PredictionAbstract
Smartphones are no doubt, one of the most essential accessories as these phones have taken over almost every solution of our everyday operations. Achieving their goals comes with a cost. The variety of Apps and increase in mobile Apps are consistently trying to make smartphones obsolete. The user experience is affected by the mass of Apps and the performance and efficiency are affected by the occupation of memory. Especially in Android OS, the inbuilt low memory killer, which is responsible for freeing memory, is obsolete. For it does not consider user needs information and frequently kills Applications that are going to be launched. In this paper, we propose an Approach of machine learning to develop a system that predicts the upcoming Apps and prelaunch them as well tweak the android User Interface with bringing predict Apps forward for usability and better user experience. We use Scikit-learn; a machine learning library for Python and implement a Decision tree classifier; a machine-learning algorithm to trained models on the previous App usage data provided by google from a smartphone. Later on, through experimentation, the results demonstrate the accuracy of the model and its feasibility to be implemented as a real-world system into an App usage manager for Android operating system.
References
Abadi, M. et al. 2016. TensorFlow: A System for Large-scale Machine Learning. Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (Berkeley, USA, 2016), 265–283.
Baeza-Yates, R. et al. 2015. Predicting The Next App That You Are Going To Use. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (New York, NY, USA, 2015), 285–294.
Chen, X. et al. 2015. Smartphone Background Activities in the Wild. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking - MobiCom ’15 (2015).
Dietterich, T.G. 2002. Machine Learning for Sequential Data: A Review. Lecture Notes in Computer Science (2002) 15–30.
Falaki, H. et al. 2010. Diversity in smartphone usage. Proceedings of the 8th international conference on Mobile systems, applications, and services - MobiSys ’10 (2010).
Gorman, M. 2004. Understanding the Linux Virtual Memory Manager. Prentice-Hall PTR (2004).
Higgins, B.D. et al. 2012. Informed mobile prefetching. Proceedings of the 10th international conference on Mobile systems, applications, and services - MobiSys ’12 (2012).
Lee, J. et al. 2017. CAS: Context-Aware Background Application Scheduling in Interactive Mobile Systems. IEEE Journal on Selected Areas in Communications. 35, 5 (2017), 1013–1029. DOI=https://doi.org/10.1109/jsac.2017.2676918.
Lee, J. et al. 2016. Context-aware Application Scheduling in Mobile Systems: What Will Users Do and Not Do Next? Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (New York, USA, 2016), 1235–1246.
Li, C. et al. 2017. Optimizing low memory killers for mobile devices using reinforcement learning. 13th International Wireless Communications and Mobile Computing Conference (IWCMC) (Jun. 2017), 2169–2174.
Li, Yuanjie, et al. "Mobileinsight: Extracting and analyzing cellular network information on smartphones." Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. 2016.
Nejati, Javad, and Aruna Balasubramanian. "An in-depth study of mobile browser performance." Proceedings of the 25th International Conference on World Wide Web. 2016.
Hsiao, Kuo-Lun. "Compulsive mobile application usage and technostress: the role of personality traits." Online Information Review (2017).
ALJAAIDI, Khaled Salmen, Omer Ali BAGAIS, and Raj Bahadur SHARMA. "Factors influencing usage of university mobile application among university students." The Journal of Asian Finance, Economics, and Business 7.10 (2020): 1129-1136.
Ben-Sasson, Ayelet, et al. "The relationship between users’ technology approaches and experiences in a child development mobile application." Health and Technology 10.5 (2020): 1079-1094.
Yap, Khong-Lim, Yung-Wey Chong, and Weixia Liu. "Enhanced handover mechanism using mobility prediction in wireless networks." PloS one 15.1 (2020): e0227982.
Zhang, Bo, and S. Shyam Sundar. "Proactive vs. reactive personalization: Can customization of privacy enhance user experience?" International Journal of Human-Computer Studies 128 (2019): 86-99.
González-Pérez, Laura Icela, et al. "Usability evaluation focused on the user experience of repositories related to energy sustainability: A Literature Mapping." Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality. 2017.
Sarker, Iqbal H., et al. "Mobile data science and intelligent apps: Concepts, ai-based modeling, and research directions." Mobile Networks and Applications 26.1 (2021): 285-303.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 The Scientific Journal of Cihan University– Sulaimaniya
This work is licensed under a Creative Commons Attribution 4.0 International License.
SJCUS's open access articles are published under a Creative Commons Attribution CC-BY-NC-ND 4.0 license.
