Enhancing Fund Recommendations with Multi-round Feedback-based Reinforcement Learning
YanShen YI , Chao WANG , Ying SUN , Qi ZHANG , Xunpeng HUANG , Hui XIONG
Mutual funds are essential financial products for investors. However, existing fund recommendation systems often struggle to capture person-alized, multi-dimensional needs like risk tolerance and investment goals, and face challenges from data privacy regulations and fragmented user interaction data. To address these limitations, we propose the Multi-round Adaptive Fund Recommendation Reinforcement Learning (MAFR-RL) framework. MAFR-RL innovatively employs a multi-round ratings-based scenario with a simulated user to overcome data lim-itations and efficiently infer feedback. We process rich multi-view fund attributes (e.g., historical profitability, risk, investment domains) using specialized encoders and self-attention mechanisms for comprehensive representation. Our actor network features a unique hierarchical Mixture-of-Experts (MoE) architecture with Mamba-based experts. This design first divides experts into shared and view-specific groups, then further employs MoE within each group for deeply adaptive feature fusion and precise policy generation. Furthermore, we introduce a novel dual-critic network, coupled with an adaptive multi-task Q-value formulation, to effectively balance fund-level appeal and constituent stock preferences. A specifically designed fund pool sampling strategy further enhances recommendation diversity and robustness. Extensive offline experiments and a small-scale online study demonstrate the effectiveness and efficiency of the MAFR-RL framework.
Fund recommendation / Finance / Reinforcement learning
Higher Education Press 2026
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