Privacy and stability improve slate recommendation with noisy scores
Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores
Machine LearningInformation Retrieval
Summary
Slate recommendation means picking a list of items to show to users, like movies or products, based on scores given by a model. The authors explore how privacy rules apply when the system both learns those scores privately and then selects items based on them. They also develop a way to check if small random changes in scores will change the chosen list, helping guarantee stability. Their tests on real datasets show their method reduces ranking changes caused by noise without hurting performance too much.
What this means in practice
- •For recommendation system engineers: Ensure privacy guarantees hold end-to-end while keeping recommendations stable despite noisy score inputs.
- •For e-commerce platform developers: Reduce changes in product rankings caused by noisy scores to maintain a reliable user experience under privacy constraints.
Authors
Sam Urmian, Qinyi Liu, Mohammad Khalil
Abstract
We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-privacy guarantee passes through selection and its audit trace by post-processing. End-to-end privacy holds only when selector inputs are public or independent, previous private outputs, or separately privacy-accounted; fixing raw state or candidate information instead yields only a conditional guarantee. Second, we derive a logged margin certificate: bounded score-induced objective movement below half the smallest greedy decision margin guarantees that the ordered slate is unchanged. Controlled fixed-margin tests show near-linear exponent scaling, with an empirical slope of $-0.220$ (95% CI $[-0.231,-0.210]$) against the independent-noise reference $-1/4$. Real-anchor experiments on OULAD, MovieLens-25M, and Amazon Musical Instruments show that greater anchor weight reduces score-noise-induced ranking churn. OULAD and EdNet certificate checks validate the implementation of the logged inequality, while closed-loop simulations show bounded target drift and setting-dependent downstream utility. The contribution is therefore a privacy-scope contract and a certifiable score-to-slate stability mechanism, not a universal utility claim.