This study introduces a fund recommendation system based on the \(\epsilon\) -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 \(\epsilon\) -greedy algorithm, where each product on the list has the probability of 1- \(\epsilon\) of being selected as an exploitation strategy, and the probability of \(\epsilon\) is chosen as the exploration strategy. We perform a series of numerical tests to evaluate the estimation performance with different values of \(\epsilon\) .
