Application of the Intelligent Recommender Systems, Mobile Computing, and Mobile Crowd Sensing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".
Deadline for manuscript submissions: 20 June 2025 | Viewed by 177
Special Issue Editors
Interests: recommender system; mobile computing; urban computing; mobile crowd sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the advancement of technology and the widespread application of big data, spatiotemporal data play a crucial role in various domains such as weather forecasting, traffic control, and smart cities. However, obtaining high-quality spatiotemporal data that cover the entire region and all time periods remains a significant challenge, especially in large-scale and complex environments. Collecting such data requires vast sensor networks and infrastructure support, and consumes substantial resources, including financial and computational power, making it difficult to obtain comprehensive real-time data. To address this issue, many studies have proposed spatiotemporal data completion and prediction methods. These approaches leverage a small amount of available sensor data, capturing the inherent temporal and spatial correlations to infer missing data. Spatiotemporal data completion techniques can effectively fill in the sensing gaps by learning and modeling the patterns of data variation over time and space, restoring missing data. Meanwhile, spatiotemporal prediction methods analyze historical data to predict the full-region sensing data for future time periods. These methods not only reduce the need for extensive data collection but also enable efficient data inference and forecasting even at low sensing rates, thus minimizing dependence on hardware and computational resources.
The main objective of this Special Issue is to bring together original publications on recent experimental research and computer simulations related to spatiotemporal data. We are particularly interested in research focused on spatiotemporal data completion and prediction. The application of spatiotemporal data is broad, serving as a key path to enhancing the intelligence of data in various fields. This list does not exclude other areas of research that fall within the scope of spatiotemporal data.
Dr. Yuanbo Xu
Dr. Wenbin Liu
Guest Editors
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Keywords
- spatiotemporal data
- mobile computing
- data mining
- mobile crowd sensing
- deep learning
- intelligent recommender systems
- smart city
- recommender systems
- mobile crowd intelligence perception
- big data
- artificial intelligence
- combinatorial optimization
- game theory
- data collection
- multi-agent reinforcement learning
- mobile user
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