Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering

Abstract

Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data. As two major concerns in negative sampling, efficiency and effectiveness are still not fully achieved by recent works that use complicate structures and overlook risk of false negative instances. In this paper, we first provide a novel understanding of negative instances by empirically observing that only a few instances are potentially important for model learning, and false negatives tend to have stable predictions over many training iterations. Above findings motivate us to simplify the model by sampling from designed memory that only stores a few important candidates and, more importantly, tackle the untouched false negative problem by favouring high-variance samples stored in memory, which achieves efficient sampling of true negatives with high-quality. Empirical results on two synthetic datasets and three real-world datasets demonstrate both robustness and superiorities of our negative sampling method.

20 pages, 7 figures, 8 tables

References in corpus (3)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Graph Convolutional Matrix Completion
• Understanding Negative Sampling in Graph Representation Learning

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• Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
• RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders
• MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation