Dynamic collaborative mining method of user perceived interest points in mobile e-commerce platform

In the process of dynamic collaborative mining of user perceived interest points on mobile e-commerce platforms, due to the lack of effective feature classification, the recall rate of interest point data in dynamic collaborative mining of interest points is low. Therefore, a dynamic collaborative mining method for user perceived interest points on mobile e-commerce platforms is proposed. Firstly, coarse grained features of user perceived interest points are initially extracted through clustering algorithms, and their feature values are further extracted using sequence feature extraction algorithms. Then, a user perceived interest prediction model is constructed, and fitting methods are used to achieve feature classification of user perceived interest points. Finally, by designing a dynamic collaborative mining model for user perceived interest points on mobile e-commerce platforms, dynamic collaborative mining is achieved. The experimental results show that the dynamic convergence change of method in this paper interest point data mining is relatively small, and the maximum recall rate is 99%, effectively improving mining performance, thereby providing more accurate and accurate personalised recommendations for mobile e-commerce platforms.