Does Negative Sampling Matter? A Review with Insights into its Theory
  and Applications
Zhen Yang Ming Ding Tinglin Huang Yukuo Cen Junshuai Song Bin Xu Yuxiao Dong Jie Tang
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.