Skip to main content
SpringerLink
Log in

Mining mobile application usage pattern for demand prediction by considering spatial and temporal relations

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

Recently, researches on smart phones have received attentions because the wide potential applications. One of interesting and useful topic is mining and predicting the users’ mobile application (App) usage behaviors. With more and more Apps installed in users’ smart phone, the users may spend much time to find the Apps they want to use by swiping the screen. App prediction systems benefit for reducing search time and launching time since the Apps which may be launched can preload in the memory before they are actually used. Although some previous studies had been proposed on the problem of App usage analysis, they recommend Apps for users only based on the frequencies of App usages. We consider that the relationship between App usage demands and users’ recent spatial and temporal behaviors may be strong. In this paper, we propose Spatial and Temporal App Recommender (STAR), a novel framework to predict and recommend the Apps for mobile users under a smart phone environment. The STAR framework consists of four major modules. We first find the meaningful and semantic location movements from the geographic GPS trajectory data by the Spatial Relation Mining Module and generate the suitable temporal segments by the Temporal Relation Mining Module. Then, we design Spatial and Temporal App Usage Pattern Mine (STAUP-Mine) algorithm to efficiently discover mobile users’ Spatial and Temporal App Usage Patterns (STAUPs). Furthermore, an App Usage Demand Prediction Module is presented to predict the following App usage demands according to the discovered STAUPs and spatial/temporal relations. To our knowledge, this is the first study to simultaneously consider the spatial movements, temporal properties and App usage behavior for mining App usage pattern and demand prediction. Through rigorous experimental analysis from two real mobile App datasets, STAR framework delivers an excellent prediction performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Agrawal R, Srikant R (1994) Fast Algorithm for Mining Association Rules. In Proceedings of The 20th International Conference on Very Large Databases (VLDB), pp. 478–499

  2. Baeza-Yates R, Jiang D, Silvestri F, Harrison B (2015) Predicting The Next App That You Are Going To Use. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM), pp. 285–294

  3. Do TMT, Gatica-Perez D (2014) Where and What: Using Smartphones to Predict Next Locations and Applications in Daily Life. Pervasive Mob Comput 12:79–91

    Article  Google Scholar 

  4. Farrahi K, Gatica-Perez D (2008) Discovering Human Routines from Cell Phone Data with Topic Models. In Proceedings of The IEEE International Symposium on Wearable Computers (ISWC), pp.29–32

  5. Gao Y, Zhang Q, Chu Y, He X, Wan J, Zhou Z, Lin J (2013) The Research and Implementation of Customised Launcher in Android. Int J Wirel Mob Comput 6(5):441–447

    Article  Google Scholar 

  6. Holland J (1975) Adaptation in Natural and Artificial System, University of Michigan Press, Ann Arbor

  7. Huang K, Zhang C, Ma X, Chen G (2012) Predicting Mobile Application Usage using Contextual Information. In Proceedings of The ACM Conference on Ubiquitous Computing (UbiComp), pp. 1059–1065

  8. Jang B-R, Noh Y, Lee S-J, Park S-B (2015) A Combination of Temporal and General Preferences for App Recommendation. In Proceedings of The International Conference on Big Data and Smart Computing (BigComp), pp. 178–185

  9. Kamisaka D, Muramatsu S, Yokoyama H, Iwamoto T (2009) Operation Prediction for Context-Aware User Interfaces of Mobile Phones. In Proceedings of The 9th Annual International Symposium on Applications and the Internet (SAINT), pp. 16–22

  10. Kim J, Mielikäinen T (2014) Conditional Log-linear Models for Mobile Application Usage Prediction. Machine Learning and Knowledge Discovery in Databases Lecture Notes in Computer Science 8724:672–687

    Google Scholar 

  11. Kurihara S, Moriyama K, Numao M (2013) Context-Aware Application Prediction and Recommendation in Mobile Devices. In Proceeding of The 12th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technologies (WI-IAT), pp. 494–500

  12. Liao Z-X, Lei P-R, Shen T-J, Li S-C, Peng W-C (2012) Mining Temporal Profiles of Mobile Applications for Usage Prediction. In Proceeding of The 12th IEEE International Conference on Data Mining Workshops (ICDM), pp. 890–893

  13. Liao Z-X, Li S-C, Peng W-C, Yu P S (2013) On the Feature Discovery for App Usage Prediction in Smartphones. In Proceeding of The 13th IEEE International Conference on Data Mining (ICDM), pp. 1127–1132

  14. Liao Z-X, Pan Y-C, Peng W-C, Lei P-R (2013) On Mining Mobile Apps Usage Behavior for Predicting Apps Usage in Smartphones. In Proceedings of The 22nd ACM International Conference on Information and Knowledge Management (CIKM), pp. 609–618

  15. Lu E H-C, Lin Y-W, Ciou J-B (2014) Mining Mobile Application Sequential Patterns for Usage Prediction. In Proceedings of The IEEE International Conference On Granular Computing (GrC), pp. 185–190

  16. Matsumoto M, Kiyohara R, Fukui H, Numao M, Kurihara S (2008) Proposition of The Context-Aware Interface for Cellular Phone Operations. In Proceedings of The 5th International Conference on Networked Sensing Systems (INSS), pp. 233

  17. Natarajan N, Shin D, Dhillon I S (2013) Which App Will You Use Next? Collaborative Filtering with Interactional Context. In Proceedings of The 7th ACM Conference on Recommender Systems (RecSys), pp. 201–208

  18. Parate A, Böhmer M, Chu D, Ganesan D, Marlin B M (2013) Practical Prediction and Prefetch for Faster Access to Applications on Mobile Phones. In Proceedings of The ACM International Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 275–284

  19. Shin C, Hong J-H, Dey A K (2012) Understanding and Prediction of Mobile Application Usage for Smart Phones. In Proceeding of The ACM International Conference on Ubiquitous Computing (Ubicomp), pp. 173–182

  20. Tan C, Liu Q, Chen E, Xiong H (2012) Prediction for Mobile Application Usage Patterns. In Proceedings of The Mobile Data Challenge (MDC by Nokia)

  21. Xu Y, Lin M, Lu H, Cardone G, Lane N, Chen Z, Campbell A, Choudhury T (2013) Preference, Context and Communities: A Multi-Faceted Approach to Predicting Smartphone App Usage Patterns. In Proceeding of the International Symposium on Wearable Computers (ISWC), pp. 69–76

  22. Yan T, Chu D, Ganesan D, Kansal A, Liu J (2012) Fast App Launching for Mobile Devices using Predictive User Context. In Proceeding of The 10th International Conference on Mobile Systems, Applications, and Services (MobiSys), pp. 113–126

  23. Zhang C, Ding X, Chen G, Huang K, Ma X, Yan B (2012) Nihao: A Predictive Smartphone Application Launcher. In Proceeding of The 4th International Conference on Mobile Computing, Applications and Services (MobiCASE), pp. 294–313

  24. Zheng Y, Zhang L, Ma Z, Xie X, Ma W-Y (2011) Recommending Friends and Locations based on Individual Location History. ACM Trans Web (TWEB) 5(1)

    Article  Google Scholar 

  25. Zou X, Zhang W, Li S, Pan G (2013) Prophet: What App You Wish to Use Next. In Proceedings of The ACM Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 167–170

Download references

Acknowledgements

This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. NSC101-2218-E-143-002 and MOST 103-2221-E-006-271.

Author information

Authors and Affiliations

  1. Department of Geomatics, National Cheng Kung University, Tainan City, Taiwan, Republic of China

    Eric Hsueh-Chan Lu & Ya-Wen Yang

Authors
  1. Eric Hsueh-Chan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ya-Wen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eric Hsueh-Chan Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, E.HC., Yang, YW. Mining mobile application usage pattern for demand prediction by considering spatial and temporal relations. Geoinformatica 22, 693–721 (2018). https://doi.org/10.1007/s10707-018-0322-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-018-0322-9

Keywords

Search

Navigation