Personalized fund recommendation with dynamic utility learning

Jiaxin Wei () and Jia Liu ()
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Jiaxin Wei: Xi’an Jiaotong University
Jia Liu: Xi’an Jiaotong University

Financial Innovation, 2025, vol. 11, issue 1, 1-27

Abstract: Abstract This study introduces a fund recommendation system based on the $$\epsilon$$ ϵ -greedy algorithm and an incremental learning framework. This model simulates the interaction process when customers browse the web-pages of fund products. Customers click on their preferred fund products when visiting a fund recommendation web-page. The system collects customer click sequences to continually estimate and update their utility function. The system generates product lists using the $$\epsilon$$ ϵ -greedy algorithm, where each product on the list has the probability of 1- $$\epsilon$$ ϵ of being selected as an exploitation strategy, and the probability of $$\epsilon$$ ϵ is chosen as the exploration strategy. We perform a series of numerical tests to evaluate the estimation performance with different values of $$\epsilon$$ ϵ .

Keywords: Personalized fund recommendation; $$\epsilon$$ ϵ -greedy algorithm; Dynamic utility learning; 91G15; 68T05 (search for similar items in EconPapers)
JEL-codes: C61 D81 D83 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1186/s40854-024-00720-5

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