Research on Optimizing Personalized Recommendation System of Brand Promotion Content with Deep Learning Algorithm

In view of the problems of information overload and personalized needs of users in traditional brand promotion methods, this paper proposes a personalized recommendation system using deep learning algorithm to optimize brand promotion content. The system uses collaborative filtering and neural network model as the core algorithm, and realizes accurate personalized recommendation by analyzing the historical behavior data of users and the characteristics of brand promotion content. Firstly, this paper introduces the basic principle of collaborative filtering algorithm and neural network model and its application in personalized recommendation. Secondly, by analyzing the historical behavior data of users, the collaborative filtering algorithm is used to calculate the similarity of users and screen the recommended content. At the same time, combined with the neural network model, the semantic features of the brand promotion content are extracted, so as to achieve more accurate recommendation results. The experimental results show that the proposed personalized recommendation system has achieved significant improvement in the accuracy of brand promotion content and user satisfaction. Compared with the traditional recommendation system, this system can better meet the individual needs of users, improve the effect of brand promotion and user participation, and provide new ideas and methods for brand marketing.