融合多模态与差分隐私的短视频推荐系统
Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy
- 用深度学习融合视频图文音多模态特征提升推荐精度
- 设计适用于推荐场景的差分隐私机制，兼顾隐私与性能
- 适合关注隐私保护与推荐效果平衡的研究者
随着短视频平台的快速发展，推荐系统成为提升用户体验和平台粘性的关键技术。然而，现有系统在利用图像、文本、音频等多模态信息提升推荐效果的同时，面临严重的用户隐私泄露风险。本文提出一种基于多模态信息与差分隐私保护的短视频推荐系统：首先通过深度学习模型对多模态数据进行特征提取与融合，有效提升推荐准确率；随后设计适配推荐场景的差分隐私保护机制，在保障用户数据隐私的前提下维持系统性能。实验结果表明，该方法在推荐准确率、多模态融合效果及隐私保护能力方面均优于主流现有方法，为短视频平台推荐系统的设计提供了重要参考。
原文摘要 · Abstract (English)
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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