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• W3028041409 abstract "Pairwise ranking models have been widely used to address recommendation problems. The basic idea is to learn the rank of users' preferred items through separating items into emph{positive} samples if user-item interactions exist, and emph{negative} samples otherwise. Due to the limited number of observable interactions, pairwise ranking models face serious emph{class-imbalance} issues. Our theoretical analysis shows that current sampling-based methods cause the vertex-level imbalance problem, which makes the norm of learned item embeddings towards infinite after a certain training iterations, and consequently results in vanishing gradient and affects the model inference results. We thus propose an efficient emph{underline{Vi}tal underline{N}egative underline{S}ampler} (VINS) to alleviate the class-imbalance issue for pairwise ranking model, in particular for deep learning models optimized by gradient methods. The core of VINS is a bias sampler with reject probability that will tend to accept a negative candidate with a larger degree weight than the given positive item. Evaluation results on several real datasets demonstrate that the proposed sampling method speeds up the training procedure 30% to 50% for ranking models ranging from shallow to deep, while maintaining and even improving the quality of ranking results in top-N item recommendation." @default.
• W3028041409 created "2020-05-29" @default.
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• W3028041409 date "2020-05-19" @default.
• W3028041409 modified "2023-09-27" @default.
• W3028041409 title "Addressing Class-Imbalance Problem in Personalized Ranking" @default.
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• W3028041409 hasPublicationYear "2020" @default.
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