[1]CHENG Pengchao,DU Junping,XUE Zhe.Prediction of user financial behavior based on multi-way crossing[J].CAAI Transactions on Intelligent Systems,2021,16(2):378-384.[doi:10.11992/tis.202006054]
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Prediction of user financial behavior based on multi-way crossing
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
16
Number of periods:
2021 2
Page number:
378-384
Column:
吴文俊人工智能科学技术奖论坛
Public date:
2021-03-05
- Title:
- Prediction of user financial behavior based on multi-way crossing
- Keywords:
- behavior prediction; financial; multi-way crossing; residual; multi-tower model; pre-training; mining; joint training
- CLC:
- TP391
- DOI:
- 10.11992/tis.202006054
- Abstract:
- To improve the service mode and service quality in the financial field by mining the financial behaviors of users, a user financial behavior prediction algorithm based on multi-way crossing (MCUP) is proposed in this paper. First, the training features are constructed based on the attributes contained in the data. Based on the FM model, the downstream behavior prediction tasks are used to pre-train the features of the financial data, and the hidden vectors of the features are obtained. Second, the feature cross-layer is introduced to extract high-order features of financial data, overcoming the disadvantage that the FM linear model can only extract low-order features. Then, the residual network structure is used to extract high-order features of financial data, solving the gradient disappearance problem caused by the too deep network layer. Finally, a unified multi-tower model integrated by the FM, feature cross network, and residual network is used to predict user financial behavior, blending low-order and high-order features. Experimental results show that the proposed algorithm can achieve a better accuracy rate in predicting user financial behavior.
- References:
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Last Update:
2021-04-25