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- W3022416555 abstract "In personalized top-N recommender systems, a core task is to design effective methods to measure user-item preference scores and then to suggest, for each user, a small set of personalized items with high scores. However, little attention was paid to the recommendation of low-score user-item links. In this work, based on the Bayesian estimation theory, we propose a novel metric RNR (Recall-to-Noise Ratio) to characterize the ability to recommend both high-score and low-score user-item links. Then we propose a generic framework that leverages RNR to transfer the link scores of the state-of-the-art recommendation methods. Empirical experiments show that the proposed framework could optimally improve the recommendation accuracy." @default.
- W3022416555 created "2020-05-13" @default.
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- W3022416555 date "2020-04-20" @default.
- W3022416555 modified "2023-10-16" @default.
- W3022416555 title "RNR: A Generic Bayesian-based Framework for Enhancing Top-N Recommender Systems" @default.
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- W3022416555 doi "https://doi.org/10.1145/3366424.3382719" @default.
- W3022416555 hasPublicationYear "2020" @default.
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