Authors: Sejoon Oh, Berk Ustun, Julian McAuley, Srijan Kumar

Published on: February 05, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.03481

Summary

• What is new: A method named FINEST to stabilize recommender systems against data perturbations, ensuring stable recommendations.
• Why this is important: Modern recommender systems produce vastly different recommendations with slight changes in training data.
• What the research proposes: FINEST method obtains reference rank lists from a model and fine-tunes it under simulated perturbations with rank-preserving regularization.
• Results: FINEST ensures stable recommendations across a wide range of data perturbations without hurting prediction accuracy.

Technical Details

Technological frameworks used: nan

Models used: nan

Data used: Real-world datasets

Potential Impact

Healthcare, housing, finance sectors, and companies relying on recommender systems could benefit or need to adapt.

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