Multi-Selection for Recommendation Systems
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916683001626624 |
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| author | Sarmasarkar, Sahasrajit Jiang, Zhihao Goel, Ashish Korolova, Aleksandra Munagala, Kamesh |
| author_facet | Sarmasarkar, Sahasrajit Jiang, Zhihao Goel, Ashish Korolova, Aleksandra Munagala, Kamesh |
| contents | We present the construction of a multi-selection model to answer differentially private queries in the context of recommendation systems. The server sends back multiple recommendations and a ``local model'' to the user, which the user can run locally on its device to select the item that best fits its private features. We study a setup where the server uses a deep neural network (trained on the Movielens 25M dataset as the ground truth for movie recommendation. In the multi-selection paradigm, the average recommendation utility is approximately 97\% of the optimal utility (as determined by the ground truth neural network) while maintaining a local differential privacy guarantee with $ε$ ranging around 1 with respect to feature vectors of neighboring users. This is in comparison to an average recommendation utility of 91\% in the non-multi-selection regime under the same constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Selection for Recommendation Systems Sarmasarkar, Sahasrajit Jiang, Zhihao Goel, Ashish Korolova, Aleksandra Munagala, Kamesh Machine Learning We present the construction of a multi-selection model to answer differentially private queries in the context of recommendation systems. The server sends back multiple recommendations and a ``local model'' to the user, which the user can run locally on its device to select the item that best fits its private features. We study a setup where the server uses a deep neural network (trained on the Movielens 25M dataset as the ground truth for movie recommendation. In the multi-selection paradigm, the average recommendation utility is approximately 97\% of the optimal utility (as determined by the ground truth neural network) while maintaining a local differential privacy guarantee with $ε$ ranging around 1 with respect to feature vectors of neighboring users. This is in comparison to an average recommendation utility of 91\% in the non-multi-selection regime under the same constraints. |
| title | Multi-Selection for Recommendation Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.07403 |