Privacy-preserving recommender system using the data collaboration analysis for distributed datasets.
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 establi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0319954 |
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| _version_ | 1849711517993271296 |
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| author | Tomoya Yanagi Shunnosuke Ikeda Noriyoshi Sukegawa Yuichi Takano |
| author_facet | Tomoya Yanagi Shunnosuke Ikeda Noriyoshi Sukegawa Yuichi Takano |
| author_sort | Tomoya Yanagi |
| collection | DOAJ |
| description | 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. More precisely, compared to the individual analysis in which each party analyzes only its own dataset, our method reduced prediction errors by an average of 4.5% and up to 7.0%. This study opens up new possibilities for privacy-preserving techniques in recommender systems. |
| format | Article |
| id | doaj-art-c1ba9add4e7e40ca959ddb7df5d99ca7 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-c1ba9add4e7e40ca959ddb7df5d99ca72025-08-20T03:14:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01204e031995410.1371/journal.pone.0319954Privacy-preserving recommender system using the data collaboration analysis for distributed datasets.Tomoya YanagiShunnosuke IkedaNoriyoshi SukegawaYuichi TakanoIn 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. More precisely, compared to the individual analysis in which each party analyzes only its own dataset, our method reduced prediction errors by an average of 4.5% and up to 7.0%. This study opens up new possibilities for privacy-preserving techniques in recommender systems.https://doi.org/10.1371/journal.pone.0319954 |
| spellingShingle | Tomoya Yanagi Shunnosuke Ikeda Noriyoshi Sukegawa Yuichi Takano Privacy-preserving recommender system using the data collaboration analysis for distributed datasets. PLoS ONE |
| title | Privacy-preserving recommender system using the data collaboration analysis for distributed datasets. |
| title_full | Privacy-preserving recommender system using the data collaboration analysis for distributed datasets. |
| title_fullStr | Privacy-preserving recommender system using the data collaboration analysis for distributed datasets. |
| title_full_unstemmed | Privacy-preserving recommender system using the data collaboration analysis for distributed datasets. |
| title_short | Privacy-preserving recommender system using the data collaboration analysis for distributed datasets. |
| title_sort | privacy preserving recommender system using the data collaboration analysis for distributed datasets |
| url | https://doi.org/10.1371/journal.pone.0319954 |
| work_keys_str_mv | AT tomoyayanagi privacypreservingrecommendersystemusingthedatacollaborationanalysisfordistributeddatasets AT shunnosukeikeda privacypreservingrecommendersystemusingthedatacollaborationanalysisfordistributeddatasets AT noriyoshisukegawa privacypreservingrecommendersystemusingthedatacollaborationanalysisfordistributeddatasets AT yuichitakano privacypreservingrecommendersystemusingthedatacollaborationanalysisfordistributeddatasets |