# Multi-Selection for Recommendation Systems

arXiv: https://arxiv.org/abs/2504.07403 · HTML: https://mlsanity.com/paper/2504.07403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2025.

## Abstract (arXiv)

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.