Related papers: Multi-Selection for Recommendation Systems

Multi-Selection for Recommendation Systems

URL: http://arxiv.org/abs/2504.07403v1
Date: Thu, 10 Apr 2025 02:57:14 GMT
Title: Multi-Selection for Recommendation Systems
Authors: Sahasrajit Sarmasarkar, Zhihao Jiang, Ashish Goel, Aleksandra Korolova, Kamesh Munagala,
Abstract summary: We present the construction of a multi-selection model to answer differentially private queries in the context of recommendation systems.<n>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.
Score: 52.5653572324012
License: http://creativecommons.org/licenses/by/4.0/
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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