Privacy-preserving recommender system using the data collaboration analysis for distributed datasets
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929371540881408 |
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| author | Yanagi, Tomoya Ikeda, Shunnosuke Sukegawa, Noriyoshi Takano, Yuichi |
| author_facet | Yanagi, Tomoya Ikeda, Shunnosuke Sukegawa, Noriyoshi Takano, Yuichi |
| contents | In order to provide high-quality recommendations for users, it is desirable to share and integrate multiple datasets held by different parties. However, when sharing such distributed datasets, we need to protect personal and confidential information contained in the datasets. To this end, we establish a framework for privacy-preserving recommender systems using the data collaboration analysis of distributed datasets. Numerical experiments with two public rating datasets demonstrate that our privacy-preserving method for rating prediction can improve the prediction accuracy for distributed datasets. This study opens up new possibilities for privacy-preserving techniques in recommender systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01603 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy-preserving recommender system using the data collaboration analysis for distributed datasets Yanagi, Tomoya Ikeda, Shunnosuke Sukegawa, Noriyoshi Takano, Yuichi Information Retrieval Cryptography and Security Machine Learning In order to provide high-quality recommendations for users, it is desirable to share and integrate multiple datasets held by different parties. However, when sharing such distributed datasets, we need to protect personal and confidential information contained in the datasets. To this end, we establish a framework for privacy-preserving recommender systems using the data collaboration analysis of distributed datasets. Numerical experiments with two public rating datasets demonstrate that our privacy-preserving method for rating prediction can improve the prediction accuracy for distributed datasets. This study opens up new possibilities for privacy-preserving techniques in recommender systems. |
| title | Privacy-preserving recommender system using the data collaboration analysis for distributed datasets |
| topic | Information Retrieval Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2406.01603 |