Application of Personalized Automatic Recommendation Algorithm Based on Big Data Technology—Taking Logistics as the Analysis Object

With the rapid development of big data, e-commerce logistics is also developing rapidly under such an intelligent background. However, the data of the current e-commerce platform is not updated in time, resulting in the inability to recommend personalized information to users in real time, thus affecting the user experience and reducing customer satisfaction. The reason is that the current personalized recommendation algorithm does not build an incremental update recommendation model, and cannot process the data in a timely and rapid manner. To solve this problem, a personalized automatic recommendation algorithm based on big data technology is proposed. First, the dissimilarity between users and projects in the platform is calculated, and the dissimilarity matrix is constructed. Then, the matrix is used to build the data incremental update model, namely, the personalized recommendation model; In order to improve the accuracy of recommendation, IU-UserCF recommendation algorithm is introduced; Finally, taking e-commerce logistics as the research object, this method is applied to it, and the experimental comparison is carried out. The results show that the information recommended by e-commerce to users can better meet customer needs and improve customer satisfaction after the application of personalized automatic recommendation algorithm.