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A Context-Aware Usage Prediction Approach for Smartphone Applications

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Advances in Services Computing (APSCC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9464))

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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.

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References

  1. http://blog.nielsen.com/nielsenwire/online_mobile/the-state-of-mobile-apps/

  2. Blom, J., et al.: Contextual and cultural challenges for user mobility research. CACM 48(7), 37–41 (2005)

    Article  Google Scholar 

  3. 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)

    Google Scholar 

  4. Maitland, J., et al.: Increasing the awareness of daily activity levels with pervasive computing. In: Pervasive Health Conference and Workshops (2006)

    Google Scholar 

  5. Ravi, N., et al.: Context-aware battery management for mobile phones. Pervasive Comput. Commun. (PerCom), 224–233 (2008)

    Google Scholar 

  6. Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10(4), 255–268 (2006)

    Article  Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. Matsumoto, M., et al.: Proposition of the context-aware interface for cellular phone operations. In: Proceedings of the INSS 2005, pp. 233–233 (2005)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. Xiang, L.: Recommendation System Practice, p. 6. BeiJing Youdian Publication House, BeiJing (2012)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Gang, L.: Crazy Android Textbook. Dianzi Gongye Publication, BeiJing (2011)

    Google Scholar 

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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).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jingjing Huangfu, Jian Cao & Chenyang Liu

Authors
  1. Jingjing Huangfu
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  2. Jian Cao
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  3. Chenyang Liu
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Corresponding author

Correspondence to Jian Cao .

Editor information

Editors and Affiliations

  1. School of Computer Sci,Level 4, University of Adelaide, Adelaide, South Australia, Australia

    Lina Yao

  2. School of Computer Cience & Technol, Huazhong University of Science &, Wuhan, China

    Xia Xie

  3. School of Software Technology, Dalian University of Technology, Dalian, China

    Qingchen Zhang

  4. Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada

    Laurence T. Yang

  5. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  6. School of Comp. Science and Technology, Huazhong Univ. of Science and Technology, Wuhan, China

    Hai Jin

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© 2015 Springer International Publishing Switzerland

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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

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  • DOI: https://doi.org/10.1007/978-3-319-26979-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26978-8

  • Online ISBN: 978-3-319-26979-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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