A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910859394023424 |
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| author | Tang, Xing Weng, Yunpeng Lyu, Fuyuan Liu, Dugang He, Xiuqiang |
| author_facet | Tang, Xing Weng, Yunpeng Lyu, Fuyuan Liu, Dugang He, Xiuqiang |
| contents | With the rapid growth of online investment platforms, funds can be distributed to individual customers online. The central issue is to match funds with potential customers under constraints. Most mainstream platforms adopt the recommendation formulation to tackle the problem. However, the traditional recommendation regime has its inherent drawbacks when applying the fund-matching problem with multiple constraints. In this paper, we model the fund matching under the allocation formulation. We design PTOFA, a Predict-Then-Optimize Fund Allocation framework. This data-driven framework consists of two stages, i.e., prediction and optimization, which aim to predict expected revenue based on customer behavior and optimize the impression allocation to achieve the maximum revenue under the necessary constraints, respectively. Extensive experiments on real-world datasets from an industrial online investment platform validate the effectiveness and efficiency of our solution. Additionally, the online A/B tests demonstrate PTOFA's effectiveness in the real-world fund recommendation scenario. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_03165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation Tang, Xing Weng, Yunpeng Lyu, Fuyuan Liu, Dugang He, Xiuqiang Computational Engineering, Finance, and Science Information Retrieval Machine Learning With the rapid growth of online investment platforms, funds can be distributed to individual customers online. The central issue is to match funds with potential customers under constraints. Most mainstream platforms adopt the recommendation formulation to tackle the problem. However, the traditional recommendation regime has its inherent drawbacks when applying the fund-matching problem with multiple constraints. In this paper, we model the fund matching under the allocation formulation. We design PTOFA, a Predict-Then-Optimize Fund Allocation framework. This data-driven framework consists of two stages, i.e., prediction and optimization, which aim to predict expected revenue based on customer behavior and optimize the impression allocation to achieve the maximum revenue under the necessary constraints, respectively. Extensive experiments on real-world datasets from an industrial online investment platform validate the effectiveness and efficiency of our solution. Additionally, the online A/B tests demonstrate PTOFA's effectiveness in the real-world fund recommendation scenario. |
| title | A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation |
| topic | Computational Engineering, Finance, and Science Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2503.03165 |