Dynamic update and adaptive prediction analysis of user profiles based on conditional random fields

Abstract

With the rapid development of mobile internet, user behavior data has experienced explosive growth, posing significant challenges to traditional static user profiling methods. Traditional methods often overlook the dynamic nature of user behavior, making it difficult to accurately capture changes in user interests. To address this issue, this paper proposes a user profiling method based on the Conditional Random Field (CRF) model, combining dynamic updates and adaptive predictive analysis. This method fully leverages the advantages of CRF in modeling long-range dependencies. By designing flexible adaptive feature templates and optimized training strategies, the user profiling can precisely track the evolution of user interests. Experimental results show that the model significantly outperforms traditional methods in terms of accuracy, precision, recall, and F1 score, particularly among highly active user groups. The method not only significantly improves the timeliness and accuracy of user profiles but also provides more reliable data support for personalized recommendation systems and precision marketing. By adapting to changes in user behavior trends, this method offers more effective support for various applications based on user profiles.