Research on Expanding Target User Boundary Based on Big Data Analysis

E-business system will generate a large amount of information and data all the time, and its data scale has already shown the characteristics of big data (BD). Driven by BD, the concept of e-business has undergone fundamental changes, and new changes have also taken place in the way of information management. The innovation of e-business industry services can take advantage of this change and make better use of data and information. Based on this, this article proposes a user interest mining algorithm based on constraint clustering, which can mine more potential users for e-business marketing, provide users with personalized services that can better meet their needs, and achieve the purpose of expanding the target user boundary. In the stage of providing products and services to users by using e-business websites, it is need to build a good personalized information recommendation service model in order to quickly and accurately find products and services that can meet users' needs in the vast amount of information. From the test results, it can be seen that the algorithm in this article is more accurate in user interest mining of e-business platform, and the accuracy is more than 20% higher than the traditional collaborative filtering (CF) algorithm.