A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation
This addresses the fund-matching problem for online investment platforms, offering a more constrained approach than traditional recommendation systems, but it appears incremental as it adapts existing predict-then-optimize methods to a specific domain.
The paper tackles the problem of matching funds to customers on online investment platforms by proposing a predict-then-optimize allocation framework (PTOFA) that predicts customer revenue and optimizes allocations under constraints, with experiments on real-world datasets and online A/B tests validating its effectiveness and efficiency.
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.