Mining Mobile User Preferences for Personalized Context-Aware Recommendation

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Context-aware rule learning from smartphone data: survey, challenges and future directions

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An Improved Naive Bayes Classifier-Based Noise Detection Technique for Classifying User Phone Call Behavior

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A self-adaptive approach to service deployment under mobile edge computing for autonomous driving

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Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications

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Behavior-Oriented Time Segmentation for Mining Individualized Rules of Mobile Phone Users

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Telco User Activity Level Prediction with Massive Mobile Broadband Data

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Modeling Mobile User Actions for Purchase Recommendation using Deep Memory Networks

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BehavMiner: Mining User Behaviors from Mobile Phone Data for Personalized Services

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Mobile Expert System: Exploring Context-Aware Machine Learning Rules for Personalized Decision-Making in Mobile Applications

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Accessible platforms for e‐learning: A case study

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CADEN: A Context-Aware Deep Embedding Network for Financial Opinions Mining

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Measuring user similarity using check-ins from LBSN: a mobile recommendation approach for e-commerce and security services

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Exploring the Social Learning of Taxi Drivers in Latent Vehicle-to-Vehicle Networks

Xu, T., Zhu, H., Xiong, H., Zhong, H., & Chen, E. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2915228 · Cited by 12 · see citing works

Developing a Contextually Personalized Hybrid Recommender System

Bozanta, A., & Kutlu, B. (2018) · Mobile Information Systems · doi:10.1155/2018/3258916 · Cited by 11 · see citing works

Diversifying personalized mobile multimedia application recommendations through the Latent Dirichlet Allocation and clustering optimization

Raja, D. R. K., & Pushpa, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7190-7 · Cited by 11 · see citing works

Shopping trip recommendations: A novel deep learning-enhanced global planning approach

Guo, J., He, J., & Wu, X. (2024) · Decision Support Systems · doi:10.1016/j.dss.2024.114238 · Cited by 10 · see citing works

An Approach to Modeling Call Response Behavior on Mobile Phones Based on Multi-Dimensional Contexts

Sarker, I. H., Kabir, M. A., Colman, A., & Han, J. (2017) · IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft) · doi:10.1109/mobilesoft.2017.38 · Cited by 10 · see citing works

Intelligent Mobile Applications: A Systematic Mapping Study

Rachad, T., & Idri, A. (2020) · Mobile Information Systems · doi:10.1155/2020/6715363 · Cited by 10 · see citing works

A Mobile Application Recommendation Framework by Exploiting Personal Preference with Constraints

Zhu, K., Liu, Z., Zhang, L., & Gu, X. (2017) · Mobile Information Systems · doi:10.1155/2017/4542326 · Cited by 10 · see citing works

Feedback Adaptive Learning for Medical and Educational Application Recommendation

Tekin, C., Elahi, S., & van der Schaar, M. (2020) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2020.3037224 · Cited by 10 · see citing works

Evaluating Personal Assistants on Mobile devicesPreprint

Kiseleva, J., & de Rijke, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.04524 · Cited by 13 · see citing works

Deep Learning-Based Imputation Method to Enhance Crowdsourced Data on Online Business Directory Platforms for Improved Services

Xu, D., Hu, P. J., & Fang, X. (2023) · Journal of Management Information Systems · doi:10.1080/07421222.2023.2196770 · Cited by 9 · see citing works

Push-Based Recommendations in Mobile Computing Using a Multi-Layer Contextual Approach

Hermoso, R., Ilarri, S., Trillo, R., & Rodríguez-Hernández, M. D. C. (2015) · International Conference on Advances in Mobile Computing and Multimedia (MoMM) · doi:10.1145/2837126.2837128 · Cited by 9 · see citing works

An enterprise adaptive tag extraction method based on multi-feature dynamic portrait

Li, X., Ding, X., Xie, Q., Gao, S., & Zhu, Q. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01029-z · Cited by 8 · see citing works

Matrix factorization with scale-invariant parameters

Zeng, G., Zhu, H., Liu, Q., Luo, P., Chen, E., & Zhang, T. (2015) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 7 · see citing works

Collaborative tensor–topic factorization model for personalized activity recommendation

Liu, T., Liao, J., Wang, Y., Wang, J., & Qi, Q. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-018-7019-9 · Cited by 7 · see citing works

Mobile crowdsourcing based context-aware smart alarm sound for smart living

Wang, J., Guo, Y., Han, W., Zheng, J., Peng, H., Hu, X., & Cheng, J. S. (2019) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2019.02.003 · Cited by 7 · see citing works

Content-based fabric recommendation system integrating image and text information

He, Z., Ma, Y., Xiang, J., Zhang, N., & Pan, R. (2024) · Journal of the Textile Institute · doi:10.1080/00405000.2024.2348201 · Cited by 6 · see citing works

RecStore

Silva, D. V. D. S., Silva, R. D. S., & Dur�ão, F. A. (2017) · Brazilian Symposium on Multimedia and the Web · doi:10.1145/3126858.3126888 · Cited by 6 · see citing works

A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

Zhu, K., Xiao, Y., Zheng, W., Jiao, X., & Hsu, C. (2020) · IEEE Access · doi:10.1109/access.2020.3046654 · Cited by 6 · see citing works

From Data Disclosure to Privacy Nudges: A Privacy-Aware and User-Centric Personal Data Management Framework

Lü, Y., Li, S., Ioannou, A., & Tussyadiah, I. P. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-1304-6_21 · Cited by 6 · see citing works

Near Optimal Dynamic Mobile Advertisement Offloading With Time Constraints

Xu, W., Xiang, C., & Tian, C. (2019) · IEEE Access · doi:10.1109/access.2019.2934825 · Cited by 5 · see citing works

Mobile Deep Learning: Exploring Deep Neural Network for Predicting Context-Aware Smartphone Usage

Sarker, I. H., Abushark, Y. B., Khan, A. I., Alam, M. M., & Nowrozy, R. (2021) · SN Computer Science · doi:10.1007/s42979-021-00548-1 · Cited by 5 · see citing works

Leverage side information for top‐N recommendation with latent Gaussian process

Zhou, W., Li, J., Yang, Y., & Shah, F. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5534 · Cited by 5 · see citing works

A rule-based framework for developing context-aware systems for smart spaces

Uddin, I. (2019) · Nottingham ePrints (University of Nottingham) · Cited by 5 · see citing works

Mining Personal Context-Aware Preferences for Mobile Users

Zhu, H., Chen, E., Yu, K., Cao, H., Xiong, H., & Tian, J. (2012) · Proceedings · doi:10.1109/icdm.2012.31 · Cited by 50 · see citing works

Towards Personalized Context-Aware Recommendation by Mining Context Logs through Topic Models

Yu, K., Zhang, B., Zhu, H., Cao, H., & Tian, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30217-6_36 · Cited by 54 · see citing works

Context-Aware Mobile Proactive Recommendation

Liu, S., & Meng, X. (2015) · 網際網路技術學刊 · doi:10.6138/jit.2015.16.4.20130923

Inferring Contextual Preferences Using Deep Auto-Encoding

Unger, M., Shapira, B., Rokach, L., & Bar, A. (2017) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3079628.3079666 · Cited by 20 · see citing works

Context-aware Mobile Recommendation System Based on Context History

Liu, Q. (2014) · TELKOMNIKA Indonesian Journal of Electrical Engineering · doi:10.11591/telkomnika.v12i4.4786 · Cited by 8 · see citing works