A Biased Sampling Method for Imbalanced Personalized Ranking

Lu Yu, Shichao Pei, Feng Zhu, Longfei Li, Jun Zhou, Chuxu Zhang, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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 positive samples if user-item interactions exist, and negative samples otherwise. Due to the limited number of observable interactions, pairwise ranking models face serious 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 Vital Negative Sampler (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 recommendations.
Original languageEnglish (US)
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Print)9781450392365
StatePublished - Oct 17 2022
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-20


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