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A contextual collaborative approach for app usage forecasting

Yingzi Wang, Nicholas Jing Yuan, Yu Sun, Fuzheng Zhang, Xing Xie, Qi Liu, Enhong Chen
2016 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '16  
In this paper, we propose a contextual collaborative forecasting (CCF) model to address the above issues.  ...  We evaluate the model on a large real-world app usage dataset, which validates that CCF outperforms state-of-the-art methods in terms of both accuracy and efficiency for long-term app usage forecasting  ...  Forecasting the app usage trend for a user without history records is required in many real-world applications.  ... 
doi:10.1145/2971648.2971729 dblp:conf/huc/WangYSZXLC16 fatcat:2v46rcz2sne5jd6p5jezn7agz4

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  ...  [74] propose a contextual collaborative forecasting (CCF) model through tensor decomposition for app usage forecasting.  ... 
doi:10.1109/access.2019.2918325 fatcat:de763kc4qbdy5ijo55jxyhzgt4

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  
We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey.  ...  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  ...  Yong Li for all of the support and valuable discussions.  ... 
doi:10.1109/comst.2022.3163176 fatcat:yj656343ovevdldtiw6vf254ue

Prediction of Application Usage on Smartphones via Deep Learning

Abdulrahman Alruban
2022 IEEE Access  
Han et al. designed a collaborative filtering algorithm (CF), which provides a forecast for developing a collaborative filtering algorithm (CF) that predicts app cold start for new users using mobile phones  ...  OUR PROPOSED APPROACH A. DATASET A real applications usage dataset is necessary to provide scientific rigor and a basis for evaluating the application usage pattern.  ...  Abdulrahman holds a Ph.D. from the University of Plymouth, UK, in Computer Science, and a master's degree from the University of Glamorgan, UK, in Computer Systems Security.  ... 
doi:10.1109/access.2022.3171579 fatcat:u323icnoizcs7ovhq6esrldffm

Collaborative Nowcasting for Contextual Recommendation

Yu Sun, Nicholas Jing Yuan, Xing Xie, Kieran McDonald, Rui Zhang
2016 Proceedings of the 25th International Conference on World Wide Web - WWW '16  
Extensive experiments with real-world data sets from a commercial digital assistant demonstrate the effectiveness of the collaborative nowcasting model.  ...  Specifically, the model first extracts collaborative latent factors, which summarize shared temporal structural patterns in contextual signals, and then exploits the collaborative Kalman Filter to generate  ...  usage of an app.  ... 
doi:10.1145/2872427.2874812 dblp:conf/www/SunYXMZ16 fatcat:t7mzjjxhrnbptk57ebfzfvzxr4

FITMAN Future Internet Enablers for the Sending Enterprise: a FIWARE Approach & Industrial Trialing

Óscar Lázaro, Ainara González, June Sola
2015 International Workshop on Enterprise Interoperability  
Future Internet technologies (BigData, Cloud Computing, Mobile Web Apps, etc.) offer manufacturing industries the possibility to engage in a digital transformation leveraging advanced business processes  ...  The paper will show how these enablers and high-level features represent a solid and flexible technical foundation for communities of developers and practitioners to develop digital business processes  ...  Acknowledgements This work has been partly funded by the European Commission through the Project FITMAN: Future internet technologies for Manufacturing (Grant Agreement No. FP7 604674).  ... 
dblp:conf/ifip5-8/LazaroGS15 fatcat:qap7sri5vrdqnofqv3h5z7pjju

NAP: Natural App Processing for Predictive User Contexts in Mobile Smartphones

Gabriel S. Moreira, Heeseung Jo, Jinkyu Jeong
2020 Applied Sciences  
We will provide the following application prediction result and extend it to the top-k possible candidates for the next application.  ...  Neural networks have been presenting outstanding results in the state-of-the-art for mapping large sequences of data, outperforming all previous classification and prediction models.  ...  Bayesian models combine app usage with contextual features, and the features used are assumed to be independent. However, app usage is closely connected to contextual information.  ... 
doi:10.3390/app10196657 fatcat:yv4sopmchjfavdy2kgcespryva

Functionality-Based Mobile App Recommendation by Identifying Aspects from User Reviews

Xiaoying Xu, Kaushik Dutta, Anindya Datta
2014 International Conference on Information Systems  
Another main feature of our work is extracting app functionalities from textural user reviews for recommendation. Effective approach for functionality extraction is also proposed.  ...  The explosive growth of mobile apps makes it difficult for users to find their needed apps in a crowded market. Effective mechanism that provides high quality app recommendations becomes necessary.  ...  For example, Xia et al. ( 2014 ) report a multi-object approach to evolve existing mobile app RSs.  ... 
dblp:conf/icis/XuDD14 fatcat:7lnzdwzi35a7do6bkdwfyydtmi

Contextual Intent Tracking for Personal Assistants

Yu Sun, Nicholas Jing Yuan, Yingzi Wang, Xing Xie, Kieran McDonald, Rui Zhang
2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16  
The KP2 model utilizes collaborative capabilities among users, and learns for each user a personalized dynamic system that enables efficient nowcasting of users' intent.  ...  Extensive experiments using real-world data sets from a commercial personal assistant show that the KP2 model significantly outperforms various methods, and provides inspiring implications for deploying  ...  They also suggest apps that users probably need to complete certain tasks such as Uber for getting a taxi and Skype for sending a message.  ... 
doi:10.1145/2939672.2939676 dblp:conf/kdd/SunYWXMZ16 fatcat:ynalqw4n4ngtxclencogfoevuq

WhatsNextApp: LSTM-based Next-App Prediction with App Usage Sequences

Katerina Katsarou, Geunhye Yu, Felix Beierle
2022 IEEE Access  
Our approach is inspired by Word Embeddings and treats sequences of app usage logs as sequences of words.  ...  We introduce WhatsNextApp, an approach based on LSTM (Long Short-Term Memory) networks using sequences of app usage logs.  ...  The authors employ a transfer learning model based on collaborative filtering. The approach gives 83% hit rate for the top 5 apps in each location. Fang et al.  ... 
doi:10.1109/access.2022.3150874 fatcat:kmhqchie2ffj3npiwpayteeexa

FlowIntent: Detecting Privacy Leakage from User Intention to Network Traffic Mapping [article]

Hao Fu, Zizhan Zheng, Aveek K. Das, Parth H. Pathak, Pengfei Hu, Prasant Mohapatra
2016 arXiv   pre-print
In contrast to previous network-level detection schemes that mainly rely on a given set of suspicious hostnames, our approach can better adapt to the fast growth of app market and the constantly evolving  ...  Evaluation using 1002 location sharing instances collected from more than 20,000 apps shows that our approach achieves about 91% accuracy in detecting illegitimate location transmissions.  ...  Army Research Laboratory Cyber Security Collaborative Research Alliance under Contract Number W911NF-13-2-0045.  ... 
arXiv:1605.04025v2 fatcat:qpf6z5g2rvhpdbn22utwioyzsy

Did Chatbots Miss Their 'Apollo Moment'? A Survey of the Potential, Gaps and Lessons from Using Collaboration Assistants During COVID-19 [article]

Biplav Srivastava
2021 arXiv   pre-print
In this survey paper, we look at how AI in general, and collaboration assistants (CAs or chatbots for short) in particular, have been used during a true global exigency - the COVID-19 pandemic.  ...  Artificial Intelligence (AI) technologies have long been positioned as a tool to provide crucial data-driven decision support to people.  ...  cases or weather forecast.  ... 
arXiv:2103.05561v1 fatcat:xs7qsapeqzezfjdqmd5rmikk4y

The Design of a Mobile Application for Crowdsourcing in Disaster Risk Reduction

Quynh Nhu Nguyend, Antonella Frisiello, Claudio Rossi
2019 Zenodo  
The design process is integrated in the User Centred Approach, which we apply through a co-design methodology involving end-users, iterative prototyping and development phases, and five in-field evaluations  ...  Disaster Risk Reduction is a complex field in which a huge amount of data is collected and processed every day in order to plan and run preparedness and response actions, which are required to get ready  ...  The detailed list of map layers available in the app, e.g. meteorological forecasts, is omitted for brevity.  ... 
doi:10.5281/zenodo.3234730 fatcat:zqgdzg2wsnbcrme5lqxjah2dxi

C-3PO: Click-sequence-aware DeeP Neural Network (DNN)-based Pop-uPs RecOmmendation [article]

TonTon Hsien-De Huang, Hung-Yu Kao
2018 arXiv   pre-print
With the emergence of mobile and wearable devices, push notification becomes a powerful tool to connect and maintain the relationship with App users, but sending inappropriate or too many messages at the  ...  wrong time may result in the App being removed by the users.  ...  However, a major problem with this approach is that the weighting parameters connecting the hidden layer and the scoring layer are too large (for large data sets).  ... 
arXiv:1803.00458v4 fatcat:bq263mybtbbfbkqdjissyumgku

An overview of recommender systems in the internet of things

Alexander Felfernig, Seda Polat-Erdeniz, Christoph Uran, Stefan Reiterer, Muesluem Atas, Thi Ngoc Trang Tran, Paolo Azzoni, Csaba Kiraly, Koustabh Dolui
2018 Journal of Intelligent Information Systems  
The Internet Of Things (IoT) is an emerging paradigm that envisions a networked infrastructure enabling different types of devices to be interconnected.  ...  app recommendation is to implement a content-based recommendation approach where apps can be recommended for installation if their required devices (it is assumed that this information is given for each  ...  Consequently, a collaborative recommender proposes apps for Alex which have been investigated by the nearest neighbor but not by the current user (e.g., the pollution monitoring app). user-1 is the nearest  ... 
doi:10.1007/s10844-018-0530-7 fatcat:oo3iwrc2qzhgvc65efnmub5xdi
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