用预测-优化框架提升在线基金推荐匹配效率
A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation
- 先预测客户收益，再优化分配以最大化收益
- 在真实平台数据上实现更高推荐收入和效率
- 适合金融平台解决多约束下的精准匹配问题
随着在线投资平台的快速发展，基金可在线分配给个人客户。核心挑战是在多重约束下实现基金与潜在客户的精准匹配。主流平台多采用推荐范式，但该方法在处理多约束基金匹配问题时存在固有缺陷。本文提出基于分配范式的基金匹配模型，设计了数据驱动的 Predict-Then-Optimize Fund Allocation（PTOFA）框架。该框架分两阶段：第一阶段根据客户行为预测预期收益，第二阶段在满足必要约束条件下优化展示分配，以实现最大收益。在某工业级在线投资平台的真实数据集上进行的大量实验验证了该方案的有效性与高效性。此外，线上 A/B 测试进一步证明了 PTOFA 在真实基金推荐场景中的优越表现。
原文摘要 · Abstract (English)
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.
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