Abstract
With the popularity of smartphones, an increasing number of applications (app) are installed on common users’ smartphones. As a result, it is becoming difficult to find the right apps to use promptly. Based on the observation from the real data, it can be found the correlative relationship exists between the usage of app and the context, specifically, time and location contextual information. According to this analysis, a context-aware usage prediction model is proposed to predict the probability of launching apps and present this prediction by an ordered list. Furthermore, a dynamic desktop application for android platform is developed to adjust the app icon order on the desktop according to the current time and location information, which facilitates the smartphone users always capable finding their needed ones in the first page. The experiments show that our prediction model outperforms other approaches.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
http://blog.nielsen.com/nielsenwire/online_mobile/the-state-of-mobile-apps/
Blom, J., et al.: Contextual and cultural challenges for user mobility research. CACM 48(7), 37–41 (2005)
Khan, A.M., et al.: Activity recognition on smartphones via sensor-fusion and kda-based svms. Int. J. Distrib. Sens. Netw. 2014, 11581–11604 (2014)
Maitland, J., et al.: Increasing the awareness of daily activity levels with pervasive computing. In: Pervasive Health Conference and Workshops (2006)
Ravi, N., et al.: Context-aware battery management for mobile phones. Pervasive Comput. Commun. (PerCom), 224–233 (2008)
Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10(4), 255–268 (2006)
Froehlich, J.E., et al.: MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones. In: Proceedings of the of Mobisys? vol. 7, pp. 57–70 (2007)
Böhmer, M., et al.: Falling asleep with angry birds, facebook and kindle - a large scale study on mobile application usage. In: Proceedings of MobileHCI, pp. 47–56 (2011)
Matsumoto, M., et al.: Proposition of the context-aware interface for cellular phone operations. In: Proceedings of the INSS 2005, pp. 233–233 (2005)
Choonsung, S., Hong, J.-H., Dey, A.K.: Understanding and prediction of mobile application usage for smart phones. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 51–79. ACM (2012)
Paolo, C., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 5–12. ACM (2010)
Xiang, L.: Recommendation System Practice, p. 6. BeiJing Youdian Publication House, BeiJing (2012)
Peifeng, Y., et al.: App recommendation: a contest between satisfaction and temptation. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 4–11. ACM (2013)
Gang, L.: Crazy Android Textbook. Dianzi Gongye Publication, BeiJing (2011)
Acknowledgement
This work is partially supported by China National Science Foundation (Granted Number 61272438,61472253), Research Funds of Science and Technology Commission of Shanghai Municipality (Granted Number 15411952502, 12511502704).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Huangfu, J., Cao, J., Liu, C. (2015). A Context-Aware Usage Prediction Approach for Smartphone Applications. In: Yao, L., Xie, X., Zhang, Q., Yang, L., Zomaya, A., Jin, H. (eds) Advances in Services Computing. APSCC 2015. Lecture Notes in Computer Science(), vol 9464. Springer, Cham. https://doi.org/10.1007/978-3-319-26979-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-26979-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26978-8
Online ISBN: 978-3-319-26979-5
eBook Packages: Computer ScienceComputer Science (R0)