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arXiv · 2504.07403

Multi-Selection for Recommendation Systems

Sahasrajit Sarmasarkar·Zhihao Jiang·Ashish Goel·Aleksandra Korolova·Kamesh Munagala
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Abstract

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 $\epsilon$ 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.

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Sahasrajit Sarmasarkar, Zhihao Jiang, Ashish Goel, Aleksandra Korolova, Kamesh Munagala. 2025-04-10. Multi-Selection for Recommendation Systems. https://arxiv.org/abs/2504.07403

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