Multi-Selection for Recommendation Systems

Sahasrajit Sarmasarkar, Zhihao Jiang, Ashish Goel, Aleksandra Korolova, Kamesh Munagala
4/10/2025
cs.LG

No confident code implementation found for this paper. 3 possible matches can be shown below.

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 $ε$ 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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach

83% match2023

Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems

83% match2025

A Privacy Preserving System for Movie Recommendations Using Federated Learning

80% match2023

Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy

79% match2025

Vertical Federated Graph Neural Network for Recommender System

78% match1 repo82026

FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement

78% match1 repo42024

Cite this paper

@article{sarmasarkar2025multiselection,
  title  = {Multi-Selection for Recommendation Systems},
  author = {Sahasrajit Sarmasarkar and Zhihao Jiang and Ashish Goel and Aleksandra Korolova and Kamesh Munagala},
  year   = {2025},
  eprint = {2504.07403},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.07403v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2504.07403.svg)](https://codeofpaper.com/paper/2504.07403)

Discussion