Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913789199253504 |
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| author | Yang, Haowei Fu, Lei Lu, Qingyi Fan, Yue Zhang, Tianle Wang, Ruohan |
| author_facet | Yang, Haowei Fu, Lei Lu, Qingyi Fan, Yue Zhang, Tianle Wang, Ruohan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy Yang, Haowei Fu, Lei Lu, Qingyi Fan, Yue Zhang, Tianle Wang, Ruohan Information Retrieval Artificial Intelligence Cryptography and Security 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. |
| title | Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy |
| topic | Information Retrieval Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2504.08751 |