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PEVRM: Probabilistic Evolution based Version Recommendation Model for Mobile Applications
2021
IEEE Access
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Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. ...
Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. ...
In our proposed work, the recommendation is done by considering the five important parameters namely M users, N mobile Apps, a v for Apps version, r v for user rating for Apps version and finally d v version ...
doi:10.1109/access.2021.3053583
fatcat:4sc27wehrbgdvmiy5tmqutttbu
Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review
2019
IEEE Access
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This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. ...
To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback ...
Context-aware recommendation. In [88] , a context-aware recommender system for mobile apps is proposed, which utilizes a binary tensor to represent the personal usage history. Liang et al. ...
doi:10.1109/access.2019.2918325
fatcat:de763kc4qbdy5ijo55jxyhzgt4
Version-sensitive mobile App recommendation
2017
Information Sciences
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Towards this end, we propose a novel version-sensitive mobile App recommendation framework. ...
Being part and parcel of the daily life for billions of people all over the globe, the domain of mobile Applications (Apps) is the fastest growing sector of mobile market today. ...
Acknowledgments The authors are highly grateful to the anonymous referees for their careful reading and insightful comments. ...
doi:10.1016/j.ins.2016.11.025
fatcat:jlwoqumu3ndjdmf52qu2k2dnhm
A Recommender System for Mobile Applications of Google Play Store
2020
International Journal of Advanced Computer Science and Applications
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Indeed, there is a critical demand for personalized application recommendations. ...
Based on the number of installations, the number of reviews, app size, and category, we developed a content-based recommender system that can suggest some apps for users based on what they have searched ...
A recent study proposed a context-aware approach for mobile app recommendation using tensor analysis (CAMAR) [39] . ...
doi:10.14569/ijacsa.2020.0110906
fatcat:fxdhkoe4vvdzncsqznibhpk4nm
Development and assessment of Mozzify app: an integrated mHealth for Dengue reporting and mapping, health communication and behavior modification (Preprint)
2019
JMIR Formative Research
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The app's subjective quality (recommending the app to other people and the app's overall star rating), and specific quality (increase awareness, improve knowledge, and change attitudes about dengue fever ...
Some issues and suggestions were raised during the focus group and individual discussions regarding the availability of the app for Android devices, language options limitations, provision of predictive ...
This study was supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (17H01624, 19H01144) , JSPS Core-to-Core Program B Asia-Africa Science Platforms, ...
doi:10.2196/16424
pmid:31913128
fatcat:bxddzyfedjdgvgbgo3iqzrs5oe
Climbing the app wall
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
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We believe that to enable truly novel mobile app recommendation and discovery, we need to support real context-aware recommendation that utilizes the diverse range of implicit mobile data available in ...
We evaluate our approach using a dataset from an Android mobile app recommendation service called appazaar 1 . ...
Djinn improves MAP over the non-context aware method, iMF, by 28%. This clearly indicates significant benefits of using context in the mobile domain for app recommendations. ...
doi:10.1145/2396761.2398683
dblp:conf/cikm/KaratzoglouBCB12
fatcat:xqzxsh7qqzeqddgt7wmlzkwrva
Scrutinizing Mobile App Recommendation: Identifying Important App-Related Indicators
[chapter]
2016
Lecture Notes in Computer Science
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Among several traditional and novel mobile app recommender techniques that utilize a diverse set of app-related features (such as an app's Twitter followers, various version instances, etc.), which apprelated ...
features are the most important indicators for app recommendation? ...
To generate recommendations, the learned GTB predicts the rating that a user may give to an app. ...
doi:10.1007/978-3-319-48051-0_15
fatcat:ltuwv4tm5ve2ppgkm3lnmc23wq
A Knowledge Graph based Approach for Mobile Application Recommendation
[article]
2020
arXiv
pre-print
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With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. ...
To meet this challenge, we proposed a novel end-to-end Knowledge Graph Convolutional Embedding Propagation Model (KGEP) for app recommendation. ...
[6] proposed a novel version-sensitive mobile app recommendation framework by jointly exploring the version progression and dual-heterogeneous data. ...
arXiv:2009.08621v1
fatcat:vzmneguhdzhddpyas75kmyfgby
Smartphone App Usage Analysis: Datasets, Methods, and Applications
2022
IEEE Communications Surveys and Tutorials
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Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry ...
App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. ...
Yong Li for all of the support and valuable discussions. ...
doi:10.1109/comst.2022.3163176
fatcat:yj656343ovevdldtiw6vf254ue
Mobile Application Search: A QoS-Aware and Tag-Based Approach
2015
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
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The availability of enormous numbers of mobile applications (apps) is driving demand for the means to search for, recommend, and manage apps. ...
The proposed system provides two functionalities: (1) QoS-aware app search and tag-based app recommendation; and (2) tag-based app management. ...
The proposed Tag-based and QoS-aware Mobile Application Search and Management (TQMASM) provides two mechanisms: (1) QoS-aware app search and tag-based app recommendation and (2) tag-based app management ...
doi:10.4108/inis.2.4.e6
fatcat:zk2ghyyxqfbdrbxjf5ys3t7ujm
Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild
[article]
2015
arXiv
pre-print
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mobile app recommendations. ...
This paper describes a real world deployment of a context-aware mobile app recommender system (RS) called Frappe. ...
Frappé is a context-aware personalized recommender of mobile apps. ...
arXiv:1505.03014v1
fatcat:ll6p3rdyxvfipcw5dbwwxj5xqm
Health App Recommendation System using Ensemble Multimodel Deep Learning
2020
Journal of Engineering Science and Technology Review
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Nowadays, mobile devices and apps are meant to fulfill the needs of various people in society. But, mobile app Stores are facing major challenges in recommending proper apps for users. ...
Recommending mobile apps for users according to personal preference and various mobile device limitations is therefore important. ...
to improve the framework for predicting target ratings for CF items. ...
doi:10.25103/jestr.135.03
fatcat:fkxg4bijk5gpfmjq6dvo77cbte
Context-Aware User Modeling Strategies for Journey Plan Recommendation
[chapter]
2015
Lecture Notes in Computer Science
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This paper shows how we applied context-aware recommendation technologies in an existing journey planning mobile application to provide personalized and context-dependent recommendations to users. ...
We describe two different strategies for context-aware user modeling in the journey planning domain. ...
Project SU-PERHUB, funded by the European Comission (FP7-ICT-2011-7 ICT-2011.6.6, no. 289067).The authors want to specially acknowledge the Catalan Agency of Innovation and Internationalization (ACCIÓ) for ...
doi:10.1007/978-3-319-20267-9_6
fatcat:xifpevhasbamrgcrf73g7qdxue
The Need for BYOD Mobile Device Security Awareness and Training
2013
Americas Conference on Information Systems
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This paper reports the results of a survey of 131 college students entering the workforce, which demonstrates a lack of security awareness and the need for mobile device security awareness and training ...
This paper also reviews the major security concerns with mobile devices and makes some general security recommendations. ...
Malware Forecasts predict that mobile device users will download 70 billion apps in 2014 (Lookout, 2013) . ...
dblp:conf/amcis/HarrisPR13
fatcat:352q77waofdpzbduifhv4ugd5e
Smart Real-Time Recommendation of Mobile Services
2021
WSEAS transactions on systems and control
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In this paper, a new vision is presented for highly personalized, customized, and contextualized real-time recommendation of services to mobile users (consumers) by considering the current consumer-, network ...
The algorithm-driven recommended mobile services, accessible anytime-anywhere-anyhow through any kind of mobile devices via heterogeneous wireless access networks, range from typical telecommunication ...
with the VEPM model (Version Evolution Progress Model) for providing app recommendations, based on the app version's description. ...
doi:10.37394/23203.2021.16.60
fatcat:mxbxalwoijc2ziibn5miaivuv4
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