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 to check access.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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
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
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
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
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
Holland J (1975) Adaptation in Natural and Artificial System, University of Michigan Press, Ann Arbor
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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
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
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
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)
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
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.
About this article
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
- Mobile Application
- Spatial Trajectory
- Temporal Segment
- Pattern Mining
- Demand Prediction