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- W4380136713 abstract "Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance is also a vital determinant of success for personalised recommender systems to reduce the risk of losing users with low satisfaction. This study introduces a safe collaborative filtering method that prioritises recommendation quality for less-satisfied users rather than focusing on the average performance. Our approach minimises the conditional value at risk (CVaR), which represents the average risk over the tails of users' loss. To overcome computational challenges for web-scale recommender systems, we develop a robust yet practical algorithm that extends the most scalable method, implicit alternating least squares (iALS). Empirical evaluation on real-world datasets demonstrates the excellent tail performance of our approach while maintaining competitive computational efficiency." @default.
- W4380136713 created "2023-06-10" @default.
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- W4380136713 date "2023-06-08" @default.
- W4380136713 modified "2023-09-25" @default.
- W4380136713 title "Safe Collaborative Filtering" @default.
- W4380136713 doi "https://doi.org/10.48550/arxiv.2306.05292" @default.
- W4380136713 hasPublicationYear "2023" @default.
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