# A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation

arXiv: https://arxiv.org/abs/2503.03165 · HTML: https://mlsanity.com/paper/2503.03165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 15 of 15)

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- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Balanced MSE for Imbalanced Visual Regression](https://mlsanity.com/paper/2203.16427/) (2022)
- [Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation](https://mlsanity.com/paper/2307.04571/) (2023)
- [DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation](https://mlsanity.com/paper/2302.06141/) (2023)
- [Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection](https://mlsanity.com/paper/2308.12729/) (2023)
- [OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2408.08585/) (2024)
- [Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation](https://mlsanity.com/paper/2308.01208/) (2023)
- [Expected Transaction Value Optimization for Precise Marketing in FinTech Platforms](https://mlsanity.com/paper/2401.01525/) (2024)
- [Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation](https://mlsanity.com/paper/2403.17442/) (2024)
- [Towards Capacity-Aware Broker Matching: From Recommendation to Assignment](https://mlsanity.com/paper/2303.03024/) (2023)
- [Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing](https://mlsanity.com/paper/2305.01514/) (2023)

## Abstract (arXiv)

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.