Personalized fund recommendation with dynamic utility learning
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 vi...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
|
| Series: | Financial Innovation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40854-024-00720-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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 . |
|---|---|
| ISSN: | 2199-4730 |