Application of Multimodal Data Fusion in Intelligent Content Recommendation System for New Media Platforms

With the rapid development of modern information technology, social media has gradually penetrated into the daily lives of billions of people worldwide, changing people’s communication patterns. In the face of massive information overload, personalized content recommendation is an important way to solve the problem of user churn. This article explores a new social media content recommendation method that uses multimodal data fusion technology to filter and match user preferences and content features, greatly improving user experience and enhancing platform stickiness and commercial value. The experimental results show that the recommendation system using multimodal data fusion technology is significantly better than traditional methods in terms of recommendation accuracy, user satisfaction, and platform interaction rate, with a recommendation accuracy rate of $70 \%$. The research in this article has important theoretical and practical significance for promoting the development of information technology and improving the service quality of social media platforms.