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Found 6 papers, 1 papers with code
How to Diversify any Personalized Recommender? A User-centric Pre-processing approach
Our approach employs a user-centric pre-processing strategy aimed at exposing users to a wide array of content categories and topics.
When Machine Learning Models Leak: An Exploration of Synthetic Training Data
The attack assumes that the attacker can query the model to obtain predictions and that the marginal distribution of the data on which the model was trained is publicly available.
Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews
In this position paper, we make the case for applying the idea of minimal necessary data to recommender systems that use user reviews.
Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation
This paper studies user attributes in light of current concerns in the recommender system community: diversity, coverage, calibration, and data minimization.
Doing Data Right: How Lessons Learned Working with Conventional Data should Inform the Future of Synthetic Data for Recommender Systems
We present a case that the newly emerging field of synthetic data in the area of recommender systems should prioritize `doing data right'.
Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding
This paper demonstrates the potential of statistical disclosure control for protecting the data used to train recommender systems.
