Intelligent Mobile Applications: A Systematic Mapping Study

Smart mobiles as the most affordable and practical ubiquitous devices participate heavily in the enhancement of our daily life by the use of many convenient applications. However, the significant number of mobile users in addition to their heterogeneity (different profiles and contexts) obligates developers to enhance the quality of their apps by making them more intelligent and more flexible. This is realized mainly by analyzing mobile user’s data. Machine learning (ML) technology provides the methodology and techniques needed to extract knowledge from data to facilitate decision-making. Therefore, both developers and researchers affirm the benefits of combining ML techniques and mobile technology in several application fields as e-health, e-learning, e-commerce, and e-coaching. Thus, the purpose of this paper is to have an overview of the use of ML techniques in the design and development of mobile applications. Therefore, we performed a systematic mapping study of papers published on this subject in the period between 1 January 2007 and 31 December 2019. A total number of 71 papers were selected, studied, and analyzed according to the following criteria, year, sources and channel of publication, research type, and methods, kind of collected data, and finally adopted ML models, tasks, and techniques.

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[2]  Rüdiger Zarnekow,et al.  Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity , 2019, IEEE Access.

[3]  Jianhua Zou,et al.  DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage Prediction , 2019, IEEE Transactions on Mobile Computing.

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[14]  Mani B. Srivastava,et al.  Nurture: Notifying Users at the Right Time Using Reinforcement Learning , 2018, UbiComp/ISWC Adjunct.

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[16]  John Dowell,et al.  A Framework for Interaction-driven User Modeling of Mobile News Reading Behaviour , 2018, UMAP.

[17]  Hui Chen,et al.  A Sequential Recommendation for Mobile Apps: What Will User Click Next App? , 2018, 2018 IEEE International Conference on Web Services (ICWS).

[18]  Robert S H Istepanian,et al.  m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics. , 2018, Methods.

[19]  Sungyoung Lee,et al.  Model-based adaptive user interface based on context and user experience evaluation , 2018, Journal on Multimodal User Interfaces.

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[31]  Ranjitha Kumar,et al.  ERICA: Interaction Mining Mobile Apps , 2016, UIST.

[32]  Imed Zitouni,et al.  Predicting User Satisfaction with Intelligent Assistants , 2016, SIGIR.

[33]  John Herbert,et al.  A Next Application Prediction Service Using the BaranC Framework , 2016, 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC).

[34]  John Zimmerman,et al.  Planning Adaptive Mobile Experiences When Wireframing , 2016, Conference on Designing Interactive Systems.

[35]  Jorge L. V. Barbosa,et al.  A model for learning objects adaptation in light of mobile and context-aware computing , 2016, Personal and Ubiquitous Computing.

[36]  Imed Zitouni,et al.  Understanding User Satisfaction with Intelligent Assistants , 2016, CHIIR.

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[39]  Aikaterini Katmada,et al.  An adaptive serious neuro-game using a mobile version of a bio-feedback device , 2015, 2015 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL).

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[41]  Leonardo Torok,et al.  A Mobile Game Controller Adapted to the Gameplay and User's Behavior Using Machine Learning , 2015, ICEC.

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[45]  Kalle Lyytinen,et al.  Introduction to the Special Issue on Mobile Commerce: Mobile Commerce Research Yesterday, Today, Tomorrow—What Remains to Be Done? , 2015, Int. J. Electron. Commer..

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[47]  Stephan Böhm,et al.  Context-Aware Mobile Language Learning , 2015, FNC/MobiSPC.

[48]  Enhong Chen,et al.  Mining Mobile User Preferences for Personalized Context-Aware Recommendation , 2014, ACM Trans. Intell. Syst. Technol..

[49]  Rabeb Mizouni,et al.  A framework for context-aware self-adaptive mobile applications SPL , 2014, Expert Syst. Appl..

[50]  Mladjan Jovanovic,et al.  Bridging User Context and Design Models to Build Adaptive User Interfaces , 2014, HCSE.

[51]  Gregg C. Vanderheiden,et al.  Towards Deep Adaptivity - A Framework for the Development of Fully Context-Sensitive User Interfaces , 2014, HCI.

[52]  Reem Al-Nanih,et al.  Empirical Evaluation of Intelligent Mobile User Interfaces in Healthcare , 2014, Canadian Conference on AI.

[53]  Wolfgang Wörndl,et al.  Active learning strategies for exploratory mobile recommender systems , 2014, CARR '14.

[54]  Joy Bose,et al.  Contextual adaptive user interface for Android devices , 2013, 2013 Annual IEEE India Conference (INDICON).

[55]  Widodo Budiharto,et al.  The psychological aspects and implementation of adaptive games for mobile application , 2013, 2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013).

[56]  Wang Jian,et al.  Intelligent Information Processing and Data Mining in the Application of Mobile Learning , 2013, 2013 6th International Conference on Intelligent Networks and Intelligent Systems.

[57]  Sampath Deegalla,et al.  Personalized and adaptive user interface framework for mobile application , 2013, 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[58]  Daniel Schreiber,et al.  Prediction of interface preferences with a classifier selection approach , 2013, Journal on Multimodal User Interfaces.

[59]  Reem Al-Nanih,et al.  Context-based and Rule-based Adaptation of Mobile User Interfaces in mHealth , 2013, EUSPN/ICTH.

[60]  Vicente Pelechano,et al.  Exploiting User Feedback for Adapting Mobile Interaction Obtrusiveness , 2012, UCAmI.

[61]  M. Virvou,et al.  A mobile expert system for tutoring multiple languages using machine learning , 2012, 2012 International Conference on E-Learning and E-Technologies in Education (ICEEE).

[62]  Panagiotis Zervas,et al.  Delivering Adaptive and Context-Aware Educational Scenarios via Mobile Devices , 2012, 2012 IEEE 12th International Conference on Advanced Learning Technologies.

[63]  Licia Capra,et al.  Personalizing Mobile Travel Information Services , 2012 .

[64]  Jin Zhang,et al.  deStress: Mobile and remote stress monitoring, alleviation, and management platform , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[65]  Jun Yan,et al.  Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning , 2012, IEEE Transactions on Learning Technologies.

[66]  Mohammed Abdel Razek,et al.  Towards Adaptive Mobile Learning System , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[67]  Jun Yan,et al.  Modeling Mobile Learning System Using ANFIS , 2011, 2011 IEEE 11th International Conference on Advanced Learning Technologies.

[68]  Brent E. Harrison,et al.  Using sequential observations to model and predict player behavior , 2011, FDG.

[69]  Hosub Lee,et al.  An adaptive user interface based on Spatiotemporal Structure Learning , 2011, 2011 IEEE Consumer Communications and Networking Conference (CCNC).

[70]  Muhammad Ali Babar,et al.  Identifying relevant studies in software engineering , 2011, Inf. Softw. Technol..

[71]  Josh Dehlinger,et al.  Mobile Application Software Engineering : Challenges and Research Directions , 2011 .

[72]  Shian-Shyong Tseng,et al.  A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments , 2011, User Modeling and User-Adapted Interaction.

[73]  Anthony I. Wasserman,et al.  Software engineering issues for mobile application development , 2010, FoSER '10.

[74]  Ondrej Krejcar.  Adaptivity Types in Mobile User Adaptive System Framework , 2010, MOBILWARE.

[75]  Kevin Kok Wai Wong,et al.  Mobile Content Personalisation Using Intelligent User Profile Approach , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[76]  Sung-Bae Cho,et al.  A Recommendation Agent for Mobile Phone Users Using Bayesian Behavior Prediction , 2009, 2009 Third International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies.

[77]  Jun Yan,et al.  A Machine Learning Based Framework for Adaptive Mobile Learning , 2009, ICWL.

[78]  Vlado Glavinic,et al.  On Efficiency of Adaptation Algorithms for Mobile Interfaces Navigation , 2009, HCI.

[79]  Petri Saarikko,et al.  Predictive text input in a mobile shopping assistant: methods and interface design , 2009, IUI.

[80]  Rosa M. Carro,et al.  Supporting the Development of Mobile Adaptive Learning Environments: A Case Study , 2009, IEEE Transactions on Learning Technologies.

[81]  Birgitta König-Ries.  Challenges in Mobile Application Development , 2009, it Inf. Technol..

[82]  Pearl Brereton,et al.  Systematic literature reviews in software engineering - A systematic literature review , 2009, Inf. Softw. Technol..

[83]  Melanie Hartmann,et al.  Proactively Adapting Interfaces to Individual Users for Mobile Devices , 2008, AH.

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[86]  Maria Vicente A. Bonto-Kane.  Use of formal computational models for designing intelligent mobile device interfaces , 2007, Mobile HCI.

[87]  Roel Wieringa,et al.  Requirements engineering paper classification and evaluation criteria: a proposal and a discussion , 2005, Requirements Engineering.

[88]  Mary Shaw,et al.  Writing good software engineering research papers , 2003, 25th International Conference on Software Engineering, 2003. Proceedings..

[89]  한성호,et al.  Identifying mobile phone design features critical to user satisfaction , 2001 .

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