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Found 5 papers, 3 papers with code
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias
In this work, we study how DP impacts recommendation accuracy and popularity bias, when applied to the training data of state-of-the-art recommendation models.
Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing
We find that collaboration-based recommendations provide the most accurate recommendations in all scenarios.
ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations
User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process.
Position Paper on Simulating Privacy Dynamics in Recommender Systems
With this work, we hope to present perspectives on how privacy-aware simulations could be realized, such that they enable researchers to study the dynamics of privacy within a recommender system.
Robustness of Meta Matrix Factorization Against Strict Privacy Constraints
In this paper, we explore the reproducibility of MetaMF, a meta matrix factorization framework introduced by Lin et al. MetaMF employs meta learning for federated rating prediction to preserve users' privacy.
