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PEVRM: Probabilistic Evolution based Version Recommendation Model for Mobile Applications

M Maheswari, S Geetha, S Selva kumar, Marimuthu Karuppiah, Debabrata Samanta, YoHan Park
2021 IEEE Access  
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

Bin Guo, Yi Ouyang, Tong Guo, Longbing Cao, Zhiwen Yu
2019 IEEE Access  
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

Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Jialie Shen, Shunxiang Wu, Tat-Seng Chua
2017 Information Sciences  
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

Ahlam Fuad, Sahar Bayoumi, Hessah Al-Yahya
2020 International Journal of Advanced Computer Science and Applications  
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)

VON RALPH DANE MARQUEZ HERBUELA, TOMONORI KARITA, MICANALDO ERNESTO FRANCISCO, KOZO WATANABE
2019 JMIR Formative Research  
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

Alexandros Karatzoglou, Linas Baltrunas, Karen Church, Matthias Böhmer
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
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]

Jovian Lin, Kazunari Sugiyama, Min-Yen Kan, Tat-Seng Chua
2016 Lecture Notes in Computer Science  
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]

Mingwei Zhang, Jiawei Zhao, Hai Dong, Ke Deng, Ying Liu
2020 arXiv   pre-print
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

Tong Li, Tong Xia, Huandong Wang, Zhen Tu, Sasu Tarkoma, Zhu Han, Pan Hui
2022 IEEE Communications Surveys and Tutorials  
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

Shang-Pin Ma, Shin-Jie Lee, Wen-Tin Lee, Jing-Hong Lin, Jui-Hsaing Lin
2015 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems  
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]

Linas Baltrunas, Karen Church, Alexandros Karatzoglou, Nuria Oliver
2015 arXiv   pre-print
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

Deepak Chowdary Edara, Department of Computer Science & Engineering, VFSTR Deemed to be University, Vadlamudi, 522213, Guntur, Andhra Pradesh, India., Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
2020 Journal of Engineering Science and Technology Review  
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]

Victor Codina, Jose Mena, Luis Oliva
2015 Lecture Notes in Computer Science  
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

Mark A. Harris, Karen P. Patten, Elizabeth A. Regan
2013 Americas Conference on Information Systems  
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

Ivan Ganchev, Zhanlin Ji
2021 WSEAS transactions on systems and control  
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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