Does Negative Sampling Matter? A Review with Insights into its Theory and Applications
It provides a comprehensive overview for researchers and practitioners interested in negative sampling techniques, though it is incremental as a review paper.
The paper reviews negative sampling, proposing a general framework and categorizing its methods and applications across machine learning fields, but does not report specific numerical results or performance gains.
Negative sampling has swiftly risen to prominence as a focal point of research, with wide-ranging applications spanning machine learning, computer vision, natural language processing, data mining, and recommender systems. This growing interest raises several critical questions: Does negative sampling really matter? Is there a general framework that can incorporate all existing negative sampling methods? In what fields is it applied? Addressing these questions, we propose a general framework that leverages negative sampling. Delving into the history of negative sampling, we trace the development of negative sampling through five evolutionary paths. We dissect and categorize the strategies used to select negative sample candidates, detailing global, local, mini-batch, hop, and memory-based approaches. Our review categorizes current negative sampling methods into five types: static, hard, GAN-based, Auxiliary-based, and In-batch methods, providing a clear structure for understanding negative sampling. Beyond detailed categorization, we highlight the application of negative sampling in various areas, offering insights into its practical benefits. Finally, we briefly discuss open problems and future directions for negative sampling.