Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy

• URL: http://arxiv.org/abs/2504.08751v1
• Date: Thu, 27 Mar 2025 22:56:41 GMT
• Title: Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy
• Authors: Haowei Yang, Lei Fu, Qingyi Lu, Yue Fan, Tianle Zhang, Ruohan Wang,
• Abstract summary: This paper proposes a short video recommendation system based on multimodal information and differential privacy protection.<n>Deep learning models are used for feature extraction and fusion of multimodal data, effectively improving recommendation accuracy.<n>A differential privacy protection mechanism is designed to ensure user data privacy while maintaining system performance.
• Score: 9.571883876747314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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