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Smart city article details
| Title | Heterogeneous Region Embedding With Prompt Learning |
|---|---|
| ID_Doc | 28948 |
| Authors | Zhou S.; He D.; Chen L.; Shang S.; Han P. |
| Year | 2023 |
| Published | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 37 |
| DOI | http://dx.doi.org/10.1609/aaai.v37i4.25625 |
| Abstract | The prevalence of region-based urban data has opened new possibilities for exploring correlations among regions to improve urban planning and smart-city solutions. Region embedding, which plays a critical role in this endeavor, faces significant challenges related to the varying nature of city data and the effectiveness of downstream applications. In this paper, we propose a novel framework, HREP (Heterogeneous Region Embedding with Prompt learning), which addresses both intra-region and inter-region correlations through two key modules: Heterogeneous Region Embedding (HRE) and prompt learning for different downstream tasks. The HRE module constructs a heterogeneous region graph based on three categories of data, capturing inter-region contexts such as human mobility and geographic neighbors, and intra-region contexts such as POI (Point-of-Interest) information. We use relation-aware graph embedding to learn region and relation embeddings of edge types, and introduce self-attention to capture global correlations among regions. Additionally, we develop an attention-based fusion module to integrate shared information among different types of correlations. To enhance the effectiveness of region embedding in downstream tasks, we incorporate prompt learning, specifically prefix-tuning, which guides the learning of downstream tasks and results in better prediction performance. Our experiment results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art methods. Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
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