Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy
It addresses privacy concerns for users of short video platforms, but the approach appears incremental as it combines known techniques.
This paper tackles the problem of user privacy leakage in short video recommendation systems by proposing a system that uses multimodal information and differential privacy protection, achieving improved recommendation accuracy and privacy performance compared to existing methods.
With the rapid development of short video platforms, recommendation systems have become key technologies for improving user experience and enhancing platform engagement. However, while short video recommendation systems leverage multimodal information (such as images, text, and audio) to improve recommendation effectiveness, they also face the severe challenge of user privacy leakage. This paper proposes a short video recommendation system based on multimodal information and differential privacy protection. First, deep learning models are used for feature extraction and fusion of multimodal data, effectively improving recommendation accuracy. Then, a differential privacy protection mechanism suitable for recommendation scenarios is designed to ensure user data privacy while maintaining system performance. Experimental results show that the proposed method outperforms existing mainstream approaches in terms of recommendation accuracy, multimodal fusion effectiveness, and privacy protection performance, providing important insights for the design of recommendation systems for short video platforms.