A refined pushing method for financial product marketing data based on user interest mining

To improve the marketing effectiveness of financial products, the article designs a precise push method based on user interest mining. This method divides user groups using the K-means clustering algorithm and uses density parameters to reflect the level of user activity. Comprehensively mining user interests and preferences in financial products, and calculating the transition probability of user browsing message categories to construct a marketing data model. Accurate push is achieved by calculating the similarity between users, user interests, and candidate data. The experimental results show that after applying this method, the click through rate of financial products is between 93.58% and 96.69%, and the conversion rate of push results is between 0.88% and 0.95%. The activity level of users participating in activities has always remained above 95%, which verifies the effectiveness of this method.