Abstract
The recommendation of stock ranking has always been a challenging task in the financial technology (FinTech) field. Achieving an excellent stock ranking result in stock ranking recommendation (SRR) depends on mining the temporal relations within the stock and the complex spatial relations among the stocks. However, existing studies only consider the temporal relation features of stocks or introduce noise when extracting spatial relation features, which limits the performance of stock ranking recommendation tasks. To address this challenge, we propose the Dynamic Temporal Spatial Relation Network (DTSRN), which constructs a spatial relation graph with dynamic stock temporal relation features and extracts dynamic spatial relation features from different views for the stock ranking recommendation. Specifically, we construct learnable global-view and multi-view spatial graphs with stock temporal relation features and then employ efficient graph convolution operations to obtain the final stock representation. We extensively evaluate our method on two real-world datasets and compare it with several state-of-the-art approaches. The experimental results show that our proposed method outperforms the state-of-the-art baseline methods.
Y. Zhong and J. Chen—Equal contribution.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China under Grant No.62272487.
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Zhong, Y., Chen, J., Gao, J., Wang, J., Wan, Q. (2024). DTSRN: Dynamic Temporal Spatial Relation Network for Stock Ranking Recommendation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_27
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DOI: https://doi.org/10.1007/978-981-99-8145-8_27
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