Summary of Meta-transfer Learning Empowered Temporal Graph Networks For Cross-city Real Estate Appraisal, by Weijia Zhang et al.
Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal
by Weijia Zhang, Jindong Han, Hao Liu, Wei Fan, Hao Wang, Hui Xiong
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MetaTransfer framework leverages deep learning to transfer valuable knowledge from data-rich metropolises to data-scarce cities for improved real estate valuation performance. The approach models real estate transactions as a temporal event heterogeneous graph, incorporating irregular spatiotemporal correlations. A Hypernetwork-Based Multi-Task Learning module facilitates intra-city knowledge sharing and task-specific parameters generation. Additionally, the framework employs a Tri-Level Optimization Based Meta-Learning mechanism to adaptively re-weight training instances from multiple source cities, mitigating negative transfer. Experimental results on five real-world datasets demonstrate the superiority of MetaTransfer compared to eleven baseline algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to use computer models for predicting property values in small towns with limited data. It combines ideas from deep learning and graph networks to share knowledge between different cities. The approach involves creating a map of real estate transactions over time, taking into account spatial and temporal patterns. This helps the model learn how to value properties more accurately in smaller cities where there is less data available. |
Keywords
» Artificial intelligence » Deep learning » Meta learning » Multi task » Optimization » Spatiotemporal
