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What and How long: Prediction of Mobile App Engagement

Authors:

Dan PellegAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 1

Article No.: 8, Pages 1 - 38

https://doi.org/10.1145/3464301

Published: 08 September 2021 Publication History

Abstract

User engagement is crucial to the long-term success of a mobile app. Several metrics, such as dwell time, have been used for measuring user engagement. However, how to effectively predict user engagement in the context of mobile apps is still an open research question. For example, do the mobile usage contexts (e.g., time of day) in which users access mobile apps impact their dwell time? Answers to such questions could help mobile operating system and publishers to optimize advertising and service placement. In this article, we first conduct an empirical study for assessing how user characteristics, temporal features, and the short/long-term contexts contribute to gains in predicting users’ app dwell time on the population level. The comprehensive analysis is conducted on large app usage logs collected through a mobile advertising company. The dataset covers more than 12K anonymous users and 1.3 million log events. Based on the analysis, we further investigate a novel mobile app engagement prediction problem—can we predict simultaneously what app the user will use next and how long he/she will stay on that app? We propose several strategies for this joint prediction problem and demonstrate that our model can improve the performance significantly when compared with the state-of-the-art baselines. Our work can help mobile system developers in designing a better and more engagement-aware mobile app user experience.

References

[1]

Eugene Agichtein, Eric Brill, and Susan Dumais. 2006. Improving web search ranking by incorporating user behavior information. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 19–26.

Digital Library

[2]

Ricardo Baeza-Yates, Di Jiang, Fabrizio Silvestri, and Beverly Harrison. 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. ACM, 285–294.

Digital Library

[3]

Mikhail Bilenko and Ryen W. White. 2008. Mining the search trails of surfing crowds: identifying relevant websites from user activity. In Proceedings of the 17th International Conference on World Wide Web. ACM, 51–60.

[4]

Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. springer.

[5]

Veronika Bogina and Tsvi Kuflik. 2017. Incorporating dwell time in session-based recommendations with recurrent neural networks. In Proceedings of the RecTemp@ RecSys. 57–59.

[6]

Matthias Böhmer, Brent Hecht, Johannes Schöning, Antonio Krüger, and Gernot Bauer. 2011. Falling asleep with angry birds, facebook and kindle: A large scale study on mobile application usage. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, 47–56.

Digital Library

[7]

Leo Breiman. 2001. Random forests. Machine Learning 45, 1 (2001), 5–32.

Digital Library

[8]

Christopher J. C. Burges. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 2 (1998), 121–167.

Digital Library

[9]

Hong Cao and Miao Lin. 2017. Mining smartphone data for app usage prediction and recommendations: A survey. Pervasive and Mobile Computing 37, C (2017), 1–22.

[10]

Juan Pablo Carrascal and Karen Church. 2015. An in-situ study of mobile app & mobile search interactions. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 2739–2748.

Digital Library

[11]

Thomas M. Cover and Peter E. Hart. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 1 (1967), 21–27.

Digital Library

[12]

David Roxbee Cox. 1966. The statistical analysis of series of events. Monographs on Applied Probability and Statistics. Springer.

[13]

Ernesto Diaz-Aviles, Hoang Thanh Lam, Fabio Pinelli, Stefano Braghin, Yiannis Gkoufas, Michele Berlingerio, and Francesco Calabrese. 2014. Predicting user engagement in twitter with collaborative ranking. In Proceedings of the 2014 Recommender Systems Challenge. ACM, 41–46.

Digital Library

[14]

Trinh Minh Tri Do and Daniel Gatica-Perez. 2014. Where and what: Using smartphones to predict next locations and applications in daily life. Pervasive and Mobile Computing 12 (2014), 79–91.

[15]

Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, and Vladimir Vapnik. 1994. Boosting and other ensemble methods. Neural Computation 6, 6 (1994), 1289–1301.

Digital Library

[16]

Claudia Ehmke and Stephanie Wilson. 2007. Identifying web usability problems from eye-tracking data. In Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI... but not as we know it-Volume 1. British Computer Society, 119–128.

[17]

Hossein Falaki, Ratul Mahajan, Srikanth Kandula, Dimitrios Lymberopoulos, Ramesh Govindan, and Deborah Estrin. 2010. Diversity in smartphone usage. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services. ACM, 179–194.

Digital Library

[18]

Denzil Ferreira, Jorge Goncalves, Vassilis Kostakos, Louise Barkhuus, and Anind K. Dey. 2014. Contextual experience sampling of mobile application micro-usage. In Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices & Services. ACM, 91–100.

[19]

Steve Fox, Kuldeep Karnawat, Mark Mydland, Susan Dumais, and Thomas White. 2005. Evaluating implicit measures to improve web search. ACM Transactions on Information Systems 23, 2 (2005), 147–168.

Digital Library

[20]

Andrew Gelman. 2008. Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine 27, 15 (2008), 2865–2873.

[21]

Google. 2018. Select a Category for Your App or Game.

[22]

Balázs Hidasi and Domonkos Tikk. 2016. General factorization framework for context-aware recommendations. Data Mining and Knowledge Discovery 30, 2 (2016), 342–371.

Digital Library

[23]

Ryosuke Homma, Keiichi Soejima, Mitsuo Yoshida, and Kyoji Umemura. 2018. Analysis of user dwell time on non-news pages. In Proceedings of the 2018 IEEE International Conference on Big Data. IEEE, 4333–4338.

[24]

David W. Hosmer Jr, Stanley Lemeshow, and Rodney X. Sturdivant. 2013. Applied Logistic Regression. John Wiley & Sons.

[25]

Jeff Huang, Ryen W. White, and Susan Dumais. 2011. No clicks, no problem: Using cursor movements to understand and improve search. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1225–1234.

Digital Library

[26]

Ke Huang, Chunhui Zhang, Xiaoxiao Ma, and Guanling Chen. 2012. Predicting mobile application usage using contextual information. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 1059–1065.

Digital Library

[27]

Diane Kelly and Nicholas J. Belkin. 2004. Display time as implicit feedback: understanding task effects. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 377–384.

[28]

Young Hoon Kim, Dan J. Kim, and Kathy Wachter. 2013. A study of mobile user engagement (MoEN): Engagement motivations, perceived value, satisfaction, and continued engagement intention. Decision Support Systems 56, C (2013), 361–370.

[29]

Farshad Kooti, Mihajlo Grbovic, Luca Maria Aiello, Eric Bax, and Kristina Lerman. 2017. iPhone’s digital marketplace: Characterizing the big spenders. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining. ACM, 13–21.

Digital Library

[30]

Janette Lehmann, Mounia Lalmas, Elad Yom-Tov, and Georges Dupret. 2012. Models of user engagement. In Proceedings of the International Conference on User Modeling, Adaptation, and Personalization. Springer, 164–175.

Digital Library

[31]

Huoran Li and Xuan Lu. 2017. Mining device-specific apps usage patterns from large-scale android users. arXiv:1707.09252. Retrieved from https://arxiv.org/abs/1707.09252.

[32]

Huoran Li, Xuan Lu, Xuanzhe Liu, Tao Xie, Kaigui Bian, Felix Xiaozhu Lin, Qiaozhu Mei, and Feng Feng. 2015. Characterizing smartphone usage patterns from millions of android users. In Proceedings of the 2015 Internet Measurement Conference. ACM, 459–472.

Digital Library

[33]

Zhung-Xun Liao, Po-Ruey Lei, Tsu-Jou Shen, Shou-Chung Li, and Wen-Chih Peng. 2012. Mining temporal profiles of mobile applications for usage prediction. In Proceedings of the 2012 IEEE 12th International Conference onData Mining Workshops. IEEE, 890–893.

Digital Library

[34]

Zhung-Xun Liao, Shou-Chung Li, Wen-Chih Peng, S. Yu Philip, and Te-Chuan Liu. 2013. On the feature discovery for app usage prediction in smartphones. In Proceedings of the 2013 IEEE 13th International Conference onData Mining. IEEE, 1127–1132.

[35]

Zhung-Xun Liao, Yi-Chin Pan, Wen-Chih Peng, and Po-Ruey Lei. 2013. On mining mobile apps usage behavior for predicting apps usage in smartphones. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. ACM, 609–618.

Digital Library

[36]

Akhil Mathur, Nicholas D. Lane, and Fahim Kawsar. 2016. Engagement-aware computing: Modelling user engagement from mobile contexts. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 622–633.

Digital Library

[37]

Abhinav Mehrotra, Veljko Pejovic, Jo Vermeulen, Robert Hendley, and Mirco Musolesi. 2016. My phone and me: Understanding people’s receptivity to mobile notifications. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 1021–1032.

Digital Library

[38]

Cataldo Musto, Giovanni Semeraro, Marco De Gemmis, and Pasquale Lops. 2015. Word embedding techniques for content-based recommender systems: An empirical evaluation. In Proceedings of the Recsys Posters.

[39]

Klaas Nelissen, Monique Snoeck, Seppe Vanden Broucke, and Bart Baesens. 2018. Swipe and tell: Using implicit feedback to predict user engagement on tablets. ACM Transactions on Information Systems 36, 4 (2018), 1–36.

Digital Library

[40]

Wei Pan, Nadav Aharony, and Alex Pentland. 2011. Composite social network for predicting mobile apps installation. Proceedings of the AAAI Conference on Artificial Intelligence 25, 1 (2011).

[41]

Veljko Pejovic and Mirco Musolesi. 2014. InterruptMe: Designing intelligent prompting mechanisms for pervasive applications. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 897–908.

Digital Library

[42]

Thanasis Petsas, Antonis Papadogiannakis, Michalis Polychronakis, Evangelos P. Markatos, and Thomas Karagiannis. 2017. Measurement, modeling, and analysis of the mobile app ecosystem. ACM Transactions on Modeling and Performance Evaluation of Computing Systems 2, 2 (2017), 1–33.

Digital Library

[43]

Martin Pielot, Karen Church, and Rodrigo De Oliveira. 2014. An in-situ study of mobile phone notifications. In Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices & Services. ACM, 233–242.

Digital Library

[44]

Mary Lou Quinlan. 2003. Just Ask a Woman: Cracking the Code of What Women Want and How They Buy. John Wiley & Sons.

[45]

Janeaya Revels, Dewi Tojib, and Yelena Tsarenko. 2010. Understanding consumer intention to use mobile services. Australasian Marketing Journal 18, 2 (2010), 74–80.

[46]

Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural Networks 61 (2015), 85–117.

Digital Library

[47]

Yoshifumi Seki and Mitsuo Yoshida. 2018. Analysis of user dwell time by category in news application. In Proceedings of the 2018 IEEE/WIC/ACM International Conference on Web Intelligence. IEEE, 732–735.

[48]

Suranga Seneviratne, Aruna Seneviratne, Prasant Mohapatra, and Anirban Mahanti. 2015. Your installed apps reveal your gender and more!ACM SIGMOBILE Mobile Computing and Communications Review 18, 3 (2015), 55–61.

[49]

Choonsung Shin, Jin-Hyuk Hong, and Anind K. Dey. 2012. Understanding and prediction of mobile application usage for smart phones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 173–182.

[50]

Iwan Syarif, Ed Zaluska, Adam Prugel-Bennett, and Gary Wills. 2012. Application of bagging, boosting and stacking to intrusion detection. In Proceedings of the International Workshop on Machine Learning and Data Mining in Pattern Recognition. Springer, 593–602.

Digital Library

[51]

Chang Tan, Qi Liu, Enhong Chen, and Hui Xiong. 2012. Prediction for mobile application usage patterns. In Proceedings of the Nokia MDC Workshop, Vol. 12.

[52]

Maryam Tavakol and Ulf Brefeld. 2014. Factored MDPs for detecting topics of user sessions. In Proceedings of the 8th ACM Conference on Recommender Systems. 33–40.

Digital Library

[53]

Yuan Tian, Ke Zhou, Mounia Lalmas, Yiqun Liu, and Dan Pelleg. 2020. Cohort modeling based app category usage prediction. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 248–256.

Digital Library

[54]

Yuan Tian, Ke Zhou, Mounia Lalmas, and Dan Pelleg. 2020. Identifying tasks from mobile app usage patterns. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2357–2366.

Digital Library

[55]

Steven Van Canneyt, Marc Bron, Andy Haines, and Mounia Lalmas. 2017. Describing patterns and disruptions in large scale mobile app usage data. In Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 1579–1584.

Digital Library

[56]

Henriette C. van Vugt, Elly A. Konijn, Johan F. Hoorn, I. Keur, and Anton Eliéns. 2007. Realism is not all! user engagement with task-related interface characters. Interacting with Computers 19, 2 (2007), 267–280.

Digital Library

[57]

Theodore Vasiloudis, Hossein Vahabi, Ross Kravitz, and Valery Rashkov. 2017. Predicting session length in media streaming. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 977–980.

Digital Library

[58]

Tianxin Wang, Jingwu Chen, Fuzhen Zhuang, Leyu Lin, Feng Xia, Lihuan Du, and Qing He. (2020). Capturing attraction distribution: Sequential attentive network for dwell time prediction. In Proceedings of the 24th European Conference on Artificial Intelligence.

[59]

Siqi Wu, Marian-Andrei Rizoiu, and Lexing Xie. 2018. Beyond views: Measuring and predicting engagement in online videos. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 12.

[60]

Qiang Xu, Jeffrey Erman, Alexandre Gerber, Zhuoqing Mao, Jeffrey Pang, and Shobha Venkataraman. 2011. Identifying diverse usage behaviors of smartphone apps. In Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference. ACM, 329–344.

Digital Library

[61]

Shijian Xu, Wenzhong Li, Xiao Zhang, Songcheng Gao, Tong Zhan, and Sanglu Lu. 2020. Predicting and recommending the next smartphone apps based on recurrent neural network. CCF Transactions on Pervasive Computing and Interaction 2, 4 (2020), 314–328.

[62]

Ye Xu, Mu Lin, Hong Lu, Giuseppe Cardone, Nicholas Lane, Zhenyu Chen, Andrew Campbell, and Tanzeem Choudhury. 2013. Preference, context and communities: A multi-faceted approach to predicting smartphone app usage patterns. In Proceedings of the 2013 International Symposium on Wearable Computers. ACM, 69–76.

Digital Library

[63]

Tingxin Yan, David Chu, Deepak Ganesan, Aman Kansal, and Jie Liu. 2012. Fast app launching for mobile devices using predictive user context. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services. ACM, 113–126.

Digital Library

[64]

Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan Nan Liu, and Suju Rajan. 2014. Beyond clicks: Dwell time for personalization. In Proceedings of the 8th ACM Conference on Recommender Systems. ACM, 113–120.

Digital Library

[65]

Elad Yom-Tov, Mounia Lalmas, Ricardo Baeza-Yates, Georges Dupret, Janette Lehmann, and Pinar Donmez. 2013. Measuring inter-site engagement. In Proceedings of the 2013 IEEE International Conference on Big Data. IEEE, 228–236.

[66]

Chunhui Zhang, Xiang Ding, Guanling Chen, Ke Huang, Xiaoxiao Ma, and Bo Yan. 2012. Nihao: A predictive smartphone application launcher. In Proceedings of the International Conference on Mobile Computing, Applications, and Services. Springer, 294–313.

[67]

Sha Zhao, Zhiling Luo, Ziwen Jiang, Haiyan Wang, Feng Xu, Shijian Li, Jianwei Yin, and Gang Pan. 2019. AppUsage2Vec: Modeling smartphone app usage for prediction. In Proceedings of the 2019 IEEE 35th International Conference on Data Engineering. IEEE, 1322–1333.

[68]

Sha Zhao, Julian Ramos, Jianrong Tao, Ziwen Jiang, Shijian Li, Zhaohui Wu, Gang Pan, and Anind K. Dey. 2016. Discovering different kinds of smartphone users through their application usage behaviors. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 498–509.

[69]

Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Chengwei Wang, Shichen Liu, and Wenwu Ou. 2018. Jump: A joint predictor for user click and dwell time. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. AAAI Press. 3704–3710.

[70]

Xun Zou, Wangsheng Zhang, Shijian Li, and Gang Pan. 2013. Prophet: What app you wish to use next. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. ACM, 167–170.

Digital Library

Cited By

Griefahn AAvermann FZalpour CMarshall RCordon Morillas ILuedtke K(2025)Exploring the Effect of an 8-Week AI-Composed Exercise Program on Pain Intensity and Well-Being in Patients With Spinal Pain: Retrospective Cohort AnalysisJMIR Formative Research10.2196/578269(e57826-e57826)Online publication date: 18-Feb-2025
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Sun CChen JZhao YHan HJing RTan GWu D(2025)Appformer: A novel framework for mobile app usage prediction leveraging progressive multi-modal data fusion and feature extractionExpert Systems with Applications10.1016/j.eswa.2024.125903265(125903)Online publication date: Mar-2025
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Index Terms

What and How long: Prediction of Mobile App Engagement
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
2. Information systems
  1. Information systems applications
    1. Mobile information processing systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 1

January 2022

599 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3483337

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Received: 01 October 2021

Published: 08 September 2021

Accepted: 01 April 2021

Revised: 01 March 2021

Published in TOIS Volume 40, Issue 1

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