Analysis of Marketing Strategy Model Based on Network Control and Optimization Algorithms

In the era of big data, marketing strategies are undergoing profound changes, facing both challenges and nurturing new opportunities. Network control and optimization algorithms, as advanced data analysis methods, have shown great potential in promoting marketing strategy innovation. Among them, collaborative filtering algorithms are particularly prominent and widely used in various fields such as e-commerce, social media and content recommendation. This paper focuses on exploring how collaborative filtering algorithms can help optimize marketing strategy models, with the aim of improving the accuracy and execution efficiency of marketing activities. The core principle of collaborative filtering algorithm covers two branches, user collaborative filtering and project collaborative filtering, which achieve personalized recommendations by mining user behavior data. The study explores the possibility of combining data mining techniques such as clustering analysis and association rule mining with collaborative filtering algorithms, aiming to further enrich the algorithm’s functionality and optimize its application effectiveness in marketing practice. Further demonstrated the application case of collaborative filtering algorithm in practical marketing strategy optimization and discussed in depth the challenges that may be encountered during the implementation process and Corresponding solutions. The research results show that collaborative filtering algorithms can significantly improve the personalization level of marketing, enhance customer satisfaction and promote the dual improvement of marketing effectiveness and enterprise competitiveness.