Ms. Fan Zhang Profile

Fan Zhang

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Proceedings Article | 6 May 2024 Paper

Collaborative recommendation for user spatial activities in mobile environments

Ying Zhang, Fan Zhang

Proceedings Volume 13107, 131072H (2024) https://doi.org/10.1117/12.3029272

KEYWORDS: Internet, Data mining

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Recommendation technologies are facing new challenges in mobile environments due to the complexity of user behaviors in dynamic contexts. In this paper, we focus on the integration of internet content access with user natural behaviors, and propose a context-aware collaborative recommendation paradigm for user spatial activities in mobile environments. In the proposed approach, potential user behavior patterns with contexts and preferences are discovered from historical logs. Then, temporal activity prediction and service recommendation tasks are performed according to the target user’s real-time behavioral contexts using an improved collaborative filtering algorithm. Analysis and experiments indicate that our approach can effectively improve the quality of service recommendation in mobile computing environments.

Proceedings Article | 21 December 2023 Paper

A spatiotemporal neural network model for city traffic prediction

Ying Zhang, Fan Zhang

Proceedings Volume 12970, 129703R (2023) https://doi.org/10.1117/12.3012080

KEYWORDS: Feature extraction, Data modeling, Neural networks, Deep learning, Matrices, Mathematical modeling, Convolutional neural networks, Machine learning, Data processing

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The problem of traffic congestion is a significant challenge faced by smart city development. The causes of traffic congestion are complex and diverse, involving both routine factors and random factors such as unexpected events. These factors present challenges for accurate prediction of urban traffic flow. In this paper, we propose a global factor-aware spatiotemporal neural network model called GFA-STNet which addresses the cyclic, spatiotemporal, and random characteristics of urban traffic flow. We utilize deep learning to capture the spatiotemporal correlations of urban flow, employ residual networks to capture the temporal features of nearby time, periodic time, and trend time in the variation of traffic flow over time. Graph convolutional networks are used to extract the adjacency relationships between regions and combine them with external factors to extract the global spatiotemporal relationships of traffic flow, which are used to predict the traffic flow of each region. Experiments are conducted on real-world datasets, and the results show that the proposed model improves the accuracy of predictions, compared to classical traffic prediction models.

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